Machine Learning and Neural Networks - page 8

 

Toward Singularity - Neuroscience Inspiring AI




Toward Singularity - Neuroscience Inspiring AI

This video discusses the potential for artificial intelligence to reach a point of general intelligence, and the various challenges that will need to be overcome along the way.
It also discusses the potential for robots to be considered as a species, and the advantages and disadvantages of this approach.

  • 00:00:00 The brain is a complex organ that is responsible for many different tasks. Recent research has shown that the brain is also capable of performing simple tasks that were once thought to be difficult for humans. This suggests that the brain is not just an intellect, but also contains a vast amount of computational complexity.

  • 00:05:00 The author discusses the difficulty of understanding the brain at a systems level, and how scientists are using zebrafish to understand how normal brain development occurs. He goes on to say that artificial intelligence will eventually be able to grow itself more organically, based on what we learn about how the brain works.

  • 00:10:00 The brain is very different from a computer, the way it's structured. A computer basically has the CPU is separate from the memory and connecting the CPU with the memory, you have this thing called the bus, the memory bus. And the memory bus is working full time continuously when a computer is turned on. And it's actually a bottleneck. So the CPU can be very powerful, and the memory can be huge, but
    you're limited as to how much information you can transfer between the two. And that is a very limiting factor in the overall power of the standard computer. In contrast, the brain works massively in a massively parallel fashion, every single neuron is doing the best it can all the time. Even the current best AI that we have is still very, very different to the brain. It’s… you might say it's brain inspired, but it's not copying the brain. In the brain is massive amounts of feedback connections. So obviously, when we process sensory input, and that comes up into higher brain regions, and gets further processed and abstracted from the original input that we see. But there's also a massive amounts of feedback coming from those higher regions back to the perceptual areas. And this feedback directs where we look and

  • 00:15:00 The video discusses the concept of artificial intelligence, discussing the pros and cons of having it in the world. It goes on to say that AI is a promising approach, but that it will require a jump in the technology to achieve accuracy and reliability.

  • 00:20:00 The video discusses the advances in neuroscience that are being used to inspire artificial intelligence, and how this is helping to create robots that are as smart as humans. However, the technology still has a ways to go before it can be widely deployed.

  • 00:25:00 Artificial intelligence is playing a big role in the development of social robots that can understand, behave, and communicate with people in their daily lives. The world is currently designed for humans, so designing robots that have a humanoid form or an understanding of how the human world works makes it easier for those robots to integrate into society, but also to create some value and benefit without having to restructure buildings or tasks or the way the world is designed to accommodate for that human.

  • 00:30:00 The video discusses how neuroscience is inspiring advancements in AI, including deep learning and embodied cognition. Embodied cognition is the opposite of Descarte's idea that "I think, therefor I am." Robotics will eventually integrate more closely with society, and AI will become a "very useful tool" for science.

  • 00:35:00 The video discusses the idea of "general artificial intelligence" or AGI, which is the ability of a machine to achieve the level of intelligence of a competent adult human. While the validity of the "Turing test" – an exam that measures whether a machine can fool someone into thinking it is a human – is still disputed, most researchers believe that it is necessary for machines to attain this level of intelligence.

  • 00:40:00 The video discusses the potential for artificial intelligence to permeate more and more parts of our lives, and the importance of managing AI carefully so it does not start making decisions on its own. It suggests that AI will eventually become a public utility, and discusses ways in which people can have this discussion on radio and video.

  • 00:45:00 The author argues that governments must be proactive in investing in artificial intelligence and robotics, as this is an enormous investment and could have great outcomes for society. However, if not done properly, robots could lead to mass joblessness. He also notes that society will need to adapt to the coming robotics revolution, as the jobs currently done by humans will be replaced by machines.

  • 00:50:00 The author discusses the potential for artificial general intelligence and the singularity, which is the point at which machine intelligence surpasses human intelligence. They point out that although this technology is still somewhat speculative, it is likely to happen in the next 200 years. While many people may be skeptical, those in the know agree that this is definitely something that will happen.

  • 00:55:00 This video discusses the potential for artificial intelligence to reach a point of general intelligence, and the various challenges that will need to be overcome along the way. It also discusses the potential for robots to be considered as a species, and the advantages and disadvantages of this approach.

  • 01:00:00 The speaker provides an overview of the potential risks and benefits associated with advances in artificial intelligence, and discusses a hypothetical situation in which an AI goes rogue and wipes out humanity. Most researchers in the field are not concerned about this type of threat, instead focusing on the potential benefits of artificial intelligence.
Toward Singularity - Neuroscience Inspiring AI
Toward Singularity - Neuroscience Inspiring AI
  • 2023.01.08
  • www.youtube.com
Toward Singularity takes a look at how neuroscience is inspiring the development of artificial intelligence. Our amazing brain, one of the most complicated s...
 

Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 - Class Introduction & Logistics, Andrew Ng




Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 - Class Introduction & Logistics, Andrew Ng

Andrew Ng, the instructor of Stanford's CS230 Deep Learning course, introduces the course and explains the flipped classroom format. He highlights the sudden popularity of deep learning due to the increase in digital records, allowing for more effective deep learning systems. The primary goals of the course are for students to become experts in deep learning algorithms and to understand how to apply them to solve real-world problems. Ng emphasizes the importance of practical knowledge in building efficient and effective machine learning systems and hopes to systematically teach and derive machine learning algorithms while implementing them effectively with the right processes. The course will cover Convolution Neural Networks and sequence models through videos on Coursera and programming assignments on Jupyter Notebooks.

The first lecture of Stanford's CS230 Deep Learning course introduces the variety of real-world applications that will be developed through programming assignments and student projects, which can be personalized and designed to match a student's interests. Examples of past student projects range from bike price prediction to earthquake signal detection. The final project is emphasized as the most important aspect of the course, and personalized mentorship is available through the TA team and instructors. The logistics of the course are also discussed, including forming teams for group projects, taking quizzes on Coursera, and combining the course with other classes.

  • 00:00:00 In this section, Andrew Ng, the instructor of Stanford's CS230, introduces the course and explains the flipped classroom format. In this class, students will watch deeplearning.ai content on Coursera at home and participate in deeper discussions during the classroom and discussion section times. Ng introduces the teaching team, consisting of co-instructors Kian Katanforosh, the co-creator of the Deep Learning specialization, Swati Dubei, the class coordinator, Younes Mourri, the course adviser and head TA, and Aarti Bagul and Abhijeet, co-head TAs. Ng explains the sudden popularity of deep learning, stating that the digitalization of society has led to an increase in data collection, giving the students the opportunity to build more effective deep learning systems than ever before.

  • 00:05:00 In this section, Andrew Ng explains that the increase in digital records has led to a surge in data, but traditional machine learning algorithms plateau even when fed with more data. However, as neural networks become bigger, their performance keeps on getting better and better, up to a theoretical limit called the base error rate. With the advent of GPU computing and cloud services, access to large enough computation power has allowed more people to train large enough neural networks to drive high levels of accuracy in many applications. While deep learning is just one tool among many in AI, it has become so popular because it consistently delivers great results.

  • 00:10:00 In this section, the lecturer explains that there are a variety of tools and technologies that researchers use in AI in addition to deep learning, such as planning algorithms and knowledge representation. However, deep learning has taken off incredibly quickly over the past several years due to its use of massive data sets and computational power, as well as algorithmic innovation and massive investment. The primary goals of CS230 are for students to become experts in deep learning algorithms and to understand how to apply them to solve real-world problems. The lecturer, who has practical experience leading successful AI teams at Google, Baidu, and Landing AI, also emphasizes the importance of learning the practical know-how aspects of machine learning, which he says may not be covered in other academic courses.

  • 00:15:00 In this section, Andrew Ng speaks about the importance of practical knowledge in making efficient and effective decisions when building a machine learning system. He emphasizes the difference between a great software engineer and a junior one in terms of high-level judgment decisions and architectural abstractions. Similarly, he highlights the importance of knowing when to collect more data or search for hyperparameters in deep learning systems to make better decisions that can increase the team's efficiency by 2x to 10x. He aims to impart this practical knowledge to the students in the course through systematic teaching and also recommends his book, Machine Learning Yearning, for students with a bit of machine learning background.

  • 00:20:00 In this section, Andrew Ng discusses a draft of his new book called "Machine Learning Yearning" which he says is an attempt to gather the best principles for creating a systematic engineering discipline from machine learning. Ng also explains the flipped classroom format of the course where students watch videos and complete quizzes online in their own time, and attend weekly sessions for deeper interactions and discussions with TAs, Kian and himself. He goes on to talk about the importance of AI and machine learning, stating that he believes it will transform every industry much like electricity transformed several fields over a century ago.

  • 00:25:00 In this section of the video, Andrew Ng, the instructor for Stanford's CS230 Deep Learning course, expresses his hope that students will use their newfound deep learning skills to transform industries outside of the traditional tech sector, such as healthcare, civil engineering, and cosmology. He shares a valuable lesson learned through studying the rise of the internet, which is that building a website does not turn a brick and mortar business into an internet company; rather, it is the organization of the team and incorporation of internet-specific practices, such as pervasive A/B testing, that truly defines an internet company.

  • 00:30:00 In this section of the transcript, Andrew Ng discusses the differences between traditional companies and internet and AI companies. He explains that internet and AI companies tend to push decision-making power down to the engineers or engineers and product managers because these individuals are closest to the technology, algorithms, and users. Ng also mentions the importance of organizing teams to do the things that modern machine learning and deep learning allow for. Additionally, Ng describes how AI companies tend to organize data differently and specialize in spotting automation opportunities. Finally, he notes that the rise of machine learning has created new roles such as machine-learning engineer and resource-machine learning research scientist.

  • 00:35:00 In this section, Andrew Ng emphasizes the importance of effective team organization in the AI era to do more valuable work. He makes an analogy to how the software engineering world had to develop Agile development, Scrum processes, and code review to create high-performing industrial AI teams that built software effectively. Ng hopes to systematically teach and derive machine learning algorithm and implement them effectively with the right processes. Lastly, Ng guides people aspiring to learn machine learning on which classes to take to achieve their goals.

  • 00:40:00 In this section, Andrew Ng discusses the differences between CS229, CS229A, and CS230. CS229 is the most mathematical of these classes, focusing on the mathematical derivations of the algorithms. CS229A is applied machine learning, spending more time on the practical aspects and being the easiest on-ramp to machine learning, while CS230 is somewhere in between, more mathematical than CS229A but less mathematical than CS229. The unique thing about CS230 is that it focuses on deep learning, which is the hardest subset of machine learning. Andrew Ng sets accurate expectations by wanting to spend more time teaching the practical know-how of applying these algorithms, rather than focusing solely on math.

  • 00:45:00 In this section, Andrew Ng introduces the concept of AI and machine learning disappearing in the background and becoming a magical tool we can use without thinking about the learning algorithms that make it possible. He discusses the significance of machine learning in healthcare, manufacturing, agriculture, and education, where precise tutoring and feedback on coding homework assignments can be achieved using learning algorithms. The course format of CS230 involves watching deeplearning.ai videos on Coursera with additional lectures from Kian at Stanford for more in-depth knowledge and practice. The class is structured into five courses that teach students about neurons, layers, building networks, tuning networks, and industrial applications of AI.

  • 00:50:00 In this section, Andrew Ng introduces the topics covered in the course and the structure of the syllabus. The course is divided into two parts which focus on Convolution Neural Networks for imaging and videos, and sequence models, including recurrent neural networks for natural language processing and speech recognition. Each module will include videos on Coursera, quizzes and programming assignments on Jupyter Notebooks. Attendance is two percent of the final grade, eight percent on quizzes, 25 percent on programming assignments, and a significant portion on a final project.

  • 00:55:00 In this section, Andrew Ng explains the programming assignments that students will undertake during the course. Students will translate sign language images to numbers, become a Deep Learning engineer for a Happy House and create a network using the YOLOv2 object detection algorithm. They will work on optimizing a goalkeeper's shooting prediction, detect cars while driving autonomously, perform face recognition and style transfer, and create a sequence model to generate jazz music and Shakespearean poetry. The lecturer provides students with links to related papers for each of the projects.

  • 01:00:00 In this section, the speaker discusses the variety of applications that will be built in the course through programming assignments, as well as the opportunity for students to choose their own projects throughout the course. Examples of past student projects are given, including coloring black and white pictures, bike price prediction, and earthquake precursor signal detection. Students are encouraged to build and be proud of their projects, as the final project is the most important aspect of the course. The course is applied, with some math involved, and personalized mentorship is available through the TA team and instructors.

  • 01:05:00 In this section, the instructor explains the logistical details of the course, including how to create Coursera accounts, what assignments to complete, and how to form teams for the course project. The project teams will consist of one to three students, with exceptions for challenging projects. Students can combine the project with other classes as long as they discuss it with the instructor, and quizzes can be retaken on Coursera with the last submitted quiz being considered for the CS230 class.
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 - Class Introduction & Logistics, Andrew Ng
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 - Class Introduction & Logistics, Andrew Ng
  • 2019.03.21
  • www.youtube.com
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/3eJW8yTAndrew Ng is an Adjunct Pr...
 

Lecture 2 - Deep Learning Intuition



Stanford CS230: Deep Learning | Autumn 2018 | Lecture 2 - Deep Learning Intuition

First part of the lecture focuses on various applications of deep learning, including image classification, face recognition, and image style transfer. The instructor explains the importance of various factors such as dataset size, image resolution, and loss function in developing a deep learning model. The concept of encoding images using deep networks to create useful representations is also discussed, with emphasis on the triplet loss function used in face recognition. Additionally, the lecturer explains clustering using K-Means algorithm for image classification and extracting style and content from images. Overall, the section introduces students to the various techniques and considerations involved in developing successful deep learning models.

The second part of the video covers a variety of deep learning topics, such as generating images, speech recognition, and object detection. The speaker emphasizes the importance of consulting with experts when encountering problems and the critical elements of a successful deep learning project: a strategic data acquisition pipeline and architecture search and hyperparameter tuning. The video also discusses different loss functions used in deep learning, including the object detection loss function, which includes a square root to penalize errors on smaller boxes more heavily than on larger boxes. The video concludes with a recap of upcoming modules and assignments, including mandatory TA project mentorship sessions and Friday TA sections focused on neural style transfer and filling out an AWS form for potential GPU credits.

  • 00:00:00 In this section of the lecture, the goal is to give a systematic way to think about projects related to deep learning. This involves making decisions about how to collect and label data, choose architecture, and design a proper loss function for optimization. A model can be defined as an architecture plus parameters, where the architecture is the design chosen for the model and the parameters are the numbers that make the function take inputs and convert them to outputs. The loss function is used to compare the output to the ground truth, and the gradient of the loss function is computed to update the parameters to improve recognition. Many things can change within the context of deep learning, including the input, output, architecture, loss function, activation functions, optimization loop, and hyperparameters. Logistic Regression is the first architecture discussed, and an image can be represented as a 3D matrix in computer science.

  • 00:05:00 In this section of the video, the instructor discusses the basic structure of a neural network for classifying images of cats and how it can be modified to classify multiple animals through the use of multi-logistic regression. The importance of labeling the data correctly is emphasized, and the concept of one-hot encoding is introduced, with the downside of only being able to classify images with one animal mentioned. The use of Softmax as an activation function for multi-hot encoding is also mentioned, and the notation used in the course for layers and neuron indices is explained.

  • 00:10:00 In this section, the lecturer explains how deep learning extracts information from each layer in a network and how this is used for encoding of input data. He uses examples of face recognition and image classification to build intuition around concrete applications of deep learning. The lecturer also discusses the estimation of the number of images needed for a given problem and suggests that it should be based on the complexity of the task rather than the number of parameters in the network.

  • 00:15:00 In this section, the instructor discusses how to determine the amount of data needed for a deep learning project, as well as how to split the data into train, validation, and test sets. The instructor explains that the amount of data needed depends on the complexity of the task and whether the project involves indoor or outdoor images. A balanced dataset is also important to properly train the network. The resolution of the image is also discussed, with the goal being to achieve good performance while minimizing computational complexity. The instructor suggests comparing human performance at different resolutions to determine the minimum resolution needed. Ultimately, a resolution of 64 by 64 by three was determined to be sufficient for the example image used.

  • 00:20:00 In this section, the lecturer discusses a basic image classification problem where the task is to detect whether an image was taken during the day or at night. The output of the model should be a label for the image, where Y equals zero for day and Y equals one for night. The recommended architecture for this task is a shallow fully-connected or convolutional network. The loss function that should be used is the log-likelihood, which is easier to optimize than other loss functions for classification problems. The lecturer then applies this basic concept to a real-world scenario where the goal is to use face verification to validate student IDs in facilities like the gym. The dataset required for this problem would be a collection of images to compare with the images captured by the camera during ID swipe.

  • 00:25:00 In this excerpt from a lecture about deep learning, the speaker discusses the idea of using facial recognition as a means of verifying the identity of gym-goers. The speaker suggests that in order to train the system, the school would need pictures of every student, labeled with their names, as well as more photos of each student for the model's input. When discussing resolution, the speaker suggests that a higher resolution (around 400 by 400) is necessary in order to better detect details such as the distance between the eyes or the size of the nose. Finally, the speaker notes that simple distance comparisons between pixels to determine if two images are the same person won't work because of variations in lighting or other factors such as makeup or facial hair.

  • 00:30:00 In this section of the lecture, the instructor discusses the process of encoding images using a deep network to create useful representations of pictures. The goal is to create a vector that represents key features of an image, such as the distance between facial features, color, and hair. These vectors are used to compare different images of the same subject and determine a match. The instructor explains the process of minimizing the distance between the anchor and positive pictures, while maximizing the distance between the anchor and the negative picture, in order to generate a useful loss function for the deep network. The loss function is crucial for training the network to recognize specific features and make accurate predictions.

  • 00:35:00 In this section, the instructor discusses the triplet loss function used in face recognition. The loss is calculated as the L2 distance between the encoding vectors of A and P subtracted from the L2 distance between the encoding vectors of A and N. The aim is to maximize the distance between the encoding vectors of A and N, while minimizing the difference between A and P. The true loss function contains alpha, which has a specific role other than to prevent negative loss. The instructor explains that using alpha in the loss function adds weight to certain parts of the loss function. The goal is to find an encoding that represents the features of the face, and the optimization algorithm aims to minimize the loss function after multiple passes.

  • 00:40:00 In this section, the lecturer explains the use of an alpha term in the loss function of a network. This alpha term is known as the Margin, and its purpose is to penalize large weight and stabilize the network on zeros. However, it does not affect the gradient or weight. The purpose of this alpha term is to push the network to learn something meaningful instead of learning a null function. The lecturer also discusses the difference between face verification and face recognition, and suggests that adding a detection element to the pipeline can improve face recognition. A K-Nearest Neighbors algorithm can be used to compare the vectors of entered faces with vectors in a database to identify individuals.

  • 00:45:00 In this section, the instructor explains clustering, more specifically the K-Means algorithm and how it is used in image classification. He explains how the algorithm takes all the vectors in a database and clusters them into groups that look alike. This can be used to separate pictures of different people in separate folders on a phone, for example. He also discusses ways to define the K parameter in the algorithm and how different methods can be used. The instructor also discusses art generation, which involves generating an image that is the content of one image but is painted in the style of another, using data in that style. The instructor uses the Louvre Museum as an example of a content image and a painting by Claude Monet as the style image.

  • 00:50:00 In this section, the speaker discusses how to extract style and content from images using deep learning techniques. They explain that while one method is to train a network to learn one specific style, the preferred method is to learn an image instead. This involves giving a content image and extracting information about its content using a neural network trained for image recognition. To extract the style information, the speaker introduces the use of Gram matrix and explains that style is non-localized information. By combining the extracted content and style, it is possible to generate an image with the style of a given image while preserving the content. The speaker emphasizes that this technique involves backpropagating all the way back to the image and not just learning the parameters of a network.

  • 00:55:00 In this section, the instructor discusses the loss function for extracting style using the Gram matrix method and how it is computed using L2 distance between the style of the style image and generated style, as well as between the content of the content image and generated content. The instructor emphasizes that ImageNet is used in this process not for classification, but to use pre-trained parameters for the network. The focus is on training the image using white noise, and content G and style G are extracted from it by running it through the network and computing derivatives of the loss function to go back to the pixels of the image.

  • 01:00:00 In this section, the speaker discusses the process of training a network to generate an image based on the content and style images. While this network has the flexibility to work with any style and any content, it requires a new training loop each time an image is generated. The network is trained on millions of images and does not need to be trained specifically on Monet images. The loss function for this network comes from the content and style images, where the baseline is to start with white noise. The speaker then moves on to discuss an application of trigger word detection, which requires a lot of 10-second audio clips that include a positive word like "activate" and negative words like "kitchen" and "lion".

  • 01:05:00 In this section, the video discusses the process of selecting the best labeling scheme for speech recognition. The speaker explains that one should consult with an expert in speech recognition to determine the best sample rate to use for speech processing, and offers an example of a weak labeling scheme that makes it difficult to detect the trigger word in a spoken sentence. The speaker demonstrates a different labeling scheme that makes it easier for the model to detect the trigger word, but notes that it is still important to consider issues like imbalance in the dataset and the need for a sigmoid function at every time step to output zero or one.

  • 01:10:00 In this section of the video, the speaker discusses the two critical things for building a successful deep learning project. The first is to have a strategic data acquisition pipeline. One way to do this is to collect 10-second audio recordings containing positive and negative words with various accents from around campus using phones. The second critical element is architecture search and hyperparameter tuning. The speaker tells a story of how he used a Fourier transform in the beginning to extract features from speech and then talked to experts and made changes to the network based on their advice. He emphasizes that finding the right architecture is a complicated process but one that should not be given up on, and experts should be consulted.

  • 01:15:00 In this section, the speaker discusses a speech recognition problem and how he struggled with fitting a neural network onto the data until he found an expert in speech recognition who advised him on the correct use of Fourier transform hyperparameters, reducing the size of the network and using a convolution to reduce the number of time steps, as well as expanding the output. He stresses the importance of seeking advice from experts and not giving up when encountering problems during a project. The speaker then briefly mentions another way of solving chord detection by using the triplet loss algorithm and encoding audio speech in a certain vector to compare distance between those vectors. Finally, he discusses a beautiful loss function that corresponds to object detection and is used in a network called YOLO, where the loss compares x, y, width and height of bounding boxes.

  • 01:20:00 In this section of the video, the speaker discusses the object detection loss function in deep learning and why it includes a square root. The loss function includes several terms that aim to minimize the distance between the true bounding box and the predicted bounding box, as well as identify the object class within the box. The square root is included to penalize errors on smaller boxes more heavily than on larger boxes. The video concludes with a recap of upcoming modules and assignments, mandatory TA project mentorship sessions, and Friday TA sections focused on neural style transfer and filling out an AWS form for potential GPU credits.
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 2 - Deep Learning Intuition
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 2 - Deep Learning Intuition
  • 2019.03.21
  • www.youtube.com
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford Universityhttps://stanford.io/3eJW8yTAndrew NgAdjunct Professor, Computer ScienceKian K...
 

Lecture 3 - Full-Cycle Deep Learning Projects



Stanford CS230: Deep Learning | Autumn 2018 | Lecture 3 - Full-Cycle Deep Learning Projects

In this lecture on full-cycle deep learning projects, the instructor emphasizes the importance of considering all aspects of building a successful machine learning application, including problem selection, data collection, model design, testing, deployment, and maintenance. Through the example of building a voice-activated device, the instructor discusses the key components involved in deep learning projects, and encourages students to focus on feasible projects with potential positive impact and unique contributions to their respective fields. The instructor also highlights the importance of quickly collecting data, taking good notes throughout the process, and iterating during development, while also discussing specific approaches to speech activation and voice activity detection.

The second part of the lecture focuses on the importance of monitoring and maintenance in machine learning projects, particularly the need to continuously monitor and update models to ensure they perform well in the real world. The lecturer addresses the problem of data changing, which can cause machine learning models to lose accuracy, and highlights the need for constant monitoring, data collection, and model redesign to ensure that the models continue to work effectively. The lecture also discusses the impact of using a non-ML system versus a trained neural network in a voice activity detection system and suggests that hand-coded rules are generally more robust to changing data. The lecturer concludes the need to pay close attention to data privacy and obtain user consent when gathering data for retraining models.

  • 00:00:00 In this section of the video, the instructor introduces the idea of full-cycle deep learning projects by explaining the steps involved in building a successful machine learning application beyond just building a neural network model. He uses the example of building a voice-activated device and explains that the first step is to select a problem, such as using supervised learning to build the application. He also mentions the upcoming project the students will be working on that involves implementing a voice-activated device as a problem set later in the quarter.

  • 00:05:00 In this section of the lecture, the speaker discusses the key components involved in building a voice-activated device using deep learning, which includes a learning algorithm that detects trigger words such as "Alexa," "OK Google," "Hey Siri," or "Activate." The speaker outlines the important steps in building a machine learning product, starting with selecting a problem, obtaining labeled data, designing a model, testing it on a test set, deploying it, and maintaining the system. The speaker emphasizes that training a model is often an iterative process, and that building a great model involves focusing on step one, and six and seven, in addition to the core of machine learning.

  • 00:10:00 In this section of the lecture, the speaker discusses the properties of a good candidate deep learning project. He uses the example of a voice-activated device and talks about how devices like Echo and Google Home, which have the potential of being voice-activated, are difficult to configure due to the need to set them up for Wi-Fi. He provides a solution to this problem through an embedded device that can be sold to lamp makers which includes a built-in microphone and can toggle on and off through a simple voice command to the lamp itself. The speaker mentions that this project requires building a learning algorithm that can run on an embedded device and detect the wake word for turning the lamp on and off. He further suggests giving names to these devices to avoid ambiguity. The speaker indicates that while he was not working on this project, it could be a reasonable product for startups to pursue.

  • 00:15:00 In this section of the video, the presenter asks the audience what are the properties they usually look out for when selecting a deep learning project idea. He then proceeds to share his own list of five key points to consider when brainstorming project ideas. The beginning of the segment is distracted by some technical difficulties with an answering system, but the presenter eventually gets into the topic at hand, encouraging the audience to reflect on their own ideas and priorities.

  • 00:20:00 In this section of the video, Professor Ng shares his five bullet points on how to choose a deep learning project. He advises students to pick something they are genuinely interested in and consider the availability of data. Additionally, they should leverage their domain knowledge to apply machine learning techniques to unique aspects of their fields, making a unique contribution. Furthermore, he encourages choosing a project that could have a positive impact and provides utility to people without necessarily focusing on money. Lastly, he emphasizes that feasibility is a crucial factor in assessing the viability of any machine learning project or idea. Prof. Ng also gives an example of doctors and radiology students interested in deep learning, reminding them that leveraging their domain knowledge in healthcare radiology could create more unique contributions than merely starting from scratch.

  • 00:25:00 In this section, the instructor discusses the importance of choosing a feasible project and obtaining data to train the deep learning algorithm. He poses a scenario where students need to train a deep learning algorithm to detect certain phrases for a start-up project, and prompts them to estimate the number of days required for collecting data using the Fibonacci sequence method. The students are also asked to describe how they would go about collecting the required data. Technical difficulties with the presenter's laptop are encountered and using Firefox browser is suggested as an alternative.

  • 00:30:00 In this section of the video, the instructor asks the students to discuss with each other in small groups and come up with the best strategy for collecting data and deciding how many days to spend collecting data. He suggests they consider how long it will take them to train their first model and how much time they want to spend collecting data. The instructor warns that if it takes a day or two to train the first model, they may want to spend less time on data collection. He advises students to talk with their project partners to come up with a plan for data collection.

  • 00:35:00 In this section, the instructor discusses the importance of collecting data to test how the algorithm works before collecting the next dataset while working on Machine Learning projects. The instructor suggests spending 1-2 days collecting data and getting a cheap microphone to collect data by going around Stanford campus or to friends and having them say different keywords. He notes that it is difficult to know what will be hard or easy about the problem when building a new Machine Learning system. Therefore, it is essential to start with a rudimentary learning algorithm to get going.

  • 00:40:00 In this section, the speaker talks about the importance of quickly collecting data and iterating during Machine Learning development. He advises against spending too much time on data collection and suggests starting with a smaller data set first to understand what is necessary. It is critical to keep clear notes on the experiments done and the details of each model so that researchers can refer back to previous experiments rather than running them again. Additionally, he recommends doing a literature search to see what algorithms others are using in a specific field but warns that the literature may be immature in some areas.

  • 00:45:00 In this section, the lecturer discusses the importance of taking good notes throughout the deep learning process, from data collection and model design to deployment. He uses the example of deploying a speech recognition system on edge devices (such as smart speakers) to emphasize the challenges of running a large neural network on low-power processors with limited computational and power budgets. To
    address this challenge, a simpler algorithm is used to detect if anyone is even talking before passing on the audio clip to the larger neural network for classification. This simpler algorithm is known as voice activity detection (VAD) and is a standard component in many speech recognition systems, including those used in cellphones.

  • 00:50:00 In this section of the lecture, the professor poses the question of whether to use a non-machine learning-based Voice Activity Detection system or train a small neural network to recognize human speech for a project. He notes that a small neural network could be run with a low computational budget and suggests that it is easier to detect if someone is talking than to recognize the words they said. The students in the class have varying opinions, with some arguing that option one is easy to debug and simple, while option two is better for detecting noise from things like dogs barking or people whispering.

  • 00:55:00 In this section, the lecturer discusses two options for implementing speech activation, which is a problem that arises with smart speakers when there is background noise. Option one is a simple and quick solution that can be implemented in 10 minutes and involves filtering out background noise with a few lines of code. Option two is more complicated and requires building a large neural network to handle noisy environments. While option two might be necessary for large smart speaker companies, small startup teams can benefit from starting with option one and only investing in option two when it becomes necessary. The lecturer also highlights the problem of data changes when shipping a product and offers practical ideas for how to solve it.

  • 01:00:00 In this section, the speaker discusses a practical weakness in machine learning that is often ignored in academia - the problem of data changing. When machine learning models are trained on a specific dataset, they may not perform well when the data changes, such as new classes of users with accents, different background noise, or new events like a presidential scandal. The examples given include web search, self-driving cars, and factory inspections. This problem highlights the need for continuous monitoring, data collection, and model redesign to ensure that machine learning platforms continue to work in the real world.

  • 01:05:00 In this section, the class discusses which system would be more robust for VAD, voice activity detection, between a non machine-learning approach and a trained neural network. The majority of the class voted for a non-ML system. However, it turns out that training a small neural network on American accented speech makes it more probable that the neural network will pick up on certain American accent idiosyncrasies, making it less robust in detecting British accented speech. The class concludes that if a hand-coded rule can do well enough, it is generally more robust to shift data and will often generalize better, though machine learning algorithms are necessary when there isn't such a rule.

  • 01:10:00 In this section, the lecturer discusses the idea that having fewer parameters in a model can lead to better generalization, as supported by rigorous learning theory. He then poses the question of which type of deployment, cloud or edge, makes maintenance of the model easier, given that the world is constantly changing and updates may be necessary. After giving the audience time to enter their answers, the majority responded that cloud deployments make maintenance easier due to the ability to push updates and receive all data processed in one central location, albeit with issues of user privacy and security.

  • 01:15:00 In this section, the speaker discusses how monitoring and maintenance are important considerations in the deployment of machine learning projects. They emphasize that it is essential to monitor the performance and feedback of the model and to address any changes needed to improve its accuracy or to retrain the model if necessary. They also suggest that companies are setting up quality assurance processes using statistical testing to ensure that the model continues to work even if there are updates or changes. Furthermore, they highlight the importance of respecting user privacy and obtaining user consent when gathering data to use for feedback and retraining the model
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 3 - Full-Cycle Deep Learning Projects
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 3 - Full-Cycle Deep Learning Projects
  • 2019.03.21
  • www.youtube.com
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford Universityhttps://stanford.io/3eJW8yTAndrew NgAdjunct Professor, Computer ScienceKian K...
 

Lecture 4 - Adversarial Attacks / GANs




Stanford CS230: Deep Learning | Autumn 2018 | Lecture 4 - Adversarial Attacks / GANs

This lecture introduces the concept of adversarial examples, which are inputs that have been slightly modified to fool a pre-trained neural network. The lecture explains the theoretical basis of how these attacks work and discusses the malicious applications of utilizing adversarial examples in deep learning. The lecture also introduces Generative Adversarial Networks (GANs) as a way to train a model that can generate images that look like they are real, and the lecture discusses the cost function for the generator in a GAN model. The lecture concludes by explaining the logarithmic graph of the output of D when given a generated example.

The lecture covers various topics related to Generative Adversarial Networks (GANs), including tips and tricks for training GANs and their applications in image-to-image translation and unpaired generative adversarial networks using the CycleGAN architecture. The evaluation of GANs is also discussed, with methods such as human annotation, classification networks, and the Inception score and Frechet Inception Distance being popular methods for checking the realism of generated images.

  • 00:00:00 In this section, the instructor introduces the concept of adversarial attacks on neural networks and sets up the goal of finding an input image that is not an iguana, but is classified as an iguana by a pre-trained network. The instructor explains that neural networks have blind spots that make them vulnerable to these attacks, and discusses the theoretical basis of how these attacks work. The instructor emphasizes that this topic is more theoretical and lists recommended reading for further understanding.

  • 00:05:00 In this section, the speaker discusses the process of generating adversarial examples using a loss function that minimizes the difference between expected and non-expected output. The loss function can be L1, L2, or cross-entropy, depending on which works better in practice. The image is then optimized iteratively using gradient descent until it is classified as the desired output. However, the resulting image may not necessarily look like the desired output due to the vast space of possible input images that the network can see, which is considerably larger than the space of real-world images.

  • 00:10:00 In this section, the lecturer discusses the malicious applications of adversarial examples in deep learning, where attackers can use these examples to trick neural networks into misinterpreting inputs. For example, an attacker could use an adversarial example to make a picture of their face appear as someone else's, break CAPTCHAs, or bypass algorithms that detect violent content on social media. The lecturer then explains how constraining the optimization problem can make adversarial examples more dangerous, where a picture that looks like a cat to humans could be interpreted as an iguana by a neural network, which has implications for self-driving cars and other real-world applications. Finally, the initial image used for the optimization problem is discussed, with the lecturer suggesting that starting with the picture of the target object may be the most efficient strategy.

  • 00:15:00 In this section, the speaker discusses the use of RMSE error as a loss function and how it may not be an accurate way to gauge whether or not a human sees two images as similar. They also address the challenge of making a complex loss function that takes a bunch of cats and puts a minimum distance between them. The speaker then moves on to talk about adversarial examples and how the space of images that look real to humans is actually larger than the space of real images. The speaker goes on to explain non-targeted and targeted attacks and how the knowledge of the attacker is an important factor when considering different types of attacks.

  • 00:20:00 In this section of the lecture, the professor discusses ways to attack a black-box model for adversarial attacks. One idea is to use numerical gradient to estimate how the loss changes when an image is slightly perturbed. Another concept is transferability, where an adversarial example created for one model can also fool another similar model. The professor mentions potential defenses such as creating a "Safety Net" model to filter out adversarial examples, and ensembling multiple networks with different loss functions. Another approach is to train on adversarial examples along with normal examples, but this can be expensive and may not necessarily generalize to other adversarial examples.

  • 00:25:00 In this section, the lecturer discusses the complexity of utilizing adversarial examples in gradient descent optimization. The process involves propagating x through the network to compute the first term, generating an adversarial example with the optimization process, calculating the second term by forwarding propagating the adversarial example, and then using backpropagation to update the weights of the network. The technique of logit pairing is also briefly mentioned as another method for adversarial training. Theoretical perspectives on the vulnerability of neural networks to adversarial examples are also brought up, with the key argument being that linear parts of the networks, rather than high non-linearities and overfitting, are the cause of the existence of adversarial examples.

  • 00:30:00 In this section, the speaker discusses the concept of adversarial examples and how to modify an input in such a way that it radically changes the output of the network while being close to the original input. The speaker uses the derivative of y-hat with respect to x and defines the perturbation value, epsilon, and shows that by adding epsilon*w-transpose to x, we can move x by a little bit, which help in changing the output accordingly. The speaker highlights that the term w*w-transpose is always positive, and we can make this change little by changing epsilon to a small value.

  • 00:35:00 In this section, the lecturer discusses an example of how to create an adversarial attack by computing a slight change to x, called x-star, that pushes y-hat, the output of the neural network, from -4 to 0.5. The lecturer notes that if W is large, x-star will be different from x, and if the sign of W is used instead of W, the result will always push the x term to the positive side. Additionally, as x grows in dimension, the impact of the positive epsilon sign of W increases.

  • 00:40:00 In this section, the speaker discusses a method called the Fast Gradient Sign Method, which is a general way to generate adversarial examples. This method linearizes the cost function in the proximity of the parameters and is used to push the pixel images in one direction that will impact the output significantly. The speaker explains that this method works for linear as well as for deeper neural networks, as the research focuses on linearizing the behaviors of these networks. Additionally, the speaker discusses how the chain rule is used to compute the derivative of the loss function and the importance of having a high gradient to train the parameters of a neuron.

  • 00:45:00 In this section of the video, the concept of generative adversarial networks (GANs) is introduced as a way to train a model that can generate images that look like they are real, even if they have never existed before. The goal is for the network to understand the salient features of a dataset and learn to generate new images that match the real-world distribution. A minimax game is played between two networks: a generator and a discriminator. The generator starts by outputting a random image and uses feedback from the discriminator to learn how to generate more realistic images. GANs are hard to train, but the goal is for the generator to learn to mimic the real-world distribution of images with fewer parameters than the amount of data available.

  • 00:50:00 In this section, the instructor introduces the concept of Generative Adversarial Networks (GANs) and how they can be trained through backpropagation. The GAN consists of a generator and a discriminator, with the discriminator trying to identify whether an image is real or fake. The generator then generates fake images and tries to trick the discriminator into thinking they are real. The discriminator is trained using binary cross-entropy, with real images labeled as one and generated images labeled as zero. The loss function for the discriminator is JD, which has two terms: one that correctly labels real data as one, and the other is binary cross-entropy.

  • 00:55:00 In this section, the instructors talk about the cost function for the generator in a GAN model. The goal is for the generator to create realistic samples that fool the discriminator, and the cost function should reflect this. However, because it's a game, both D and G need to improve together until an equilibrium is reached. The cost function for the generator states that the discriminator should classify generated images as "one," and this is achieved by flipping the sign of the gradient. The instructors also discuss the logarithmic graph of the output of D when given a generated example.

  • 01:00:00 In this section, the instructor discusses the problem with the generator's cost function and how it goes to negative infinity, which causes the gradient to be very large when it gets closer to one. Instead, he suggests using a non-saturating cost function that has a higher gradient when closer to zero and converts the current cost function to this non-saturating cost function using a mathematical trick. The non-saturating cost function has a high gradient in the beginning when the discriminator is better than the generator, which is where they usually are early on in training.

  • 01:05:00 In this section, the speaker discusses tips and tricks for training GANs, including modifying the cost function, updating the discriminator more than the generator, and using Virtual BatchNorm. The speaker also shows examples of impressive GAN results, including using a generator to create faces with a randomized code and performing linear operations in the latent space of codes to directly impact the image space. Additionally, the speaker demonstrates how GANs can be used in image-to-image translation to generate satellite images based on map images and convert between different objects such as zebras and horses or apples and oranges.

  • 01:10:00 In this section, the instructor discusses the use of unpaired generative adversarial networks in converting horses to zebras and vice versa. The architecture used is called CycleGAN, which involves two generators and two discriminators. The generators are trained to transform an image from the source domain to the target domain and then back to the source domain. This is important in enforcing the constraint that the horse should be the same horse as a zebra, and vice versa. The loss functions used include the classic cost functions seen previously and additional terms that ensure the matching between the original and generated images.

  • 01:15:00 In this section of the video, the speakers discuss various applications of GANs, including the use of cycle costs to improve loss functions for conditional GANs, the ability to generate images based on edges or low-resolution images, and the potential for GANs to be used in privacy-preserving medical datasets and personalized manufacturing of objects like bones and dental replacements. The speakers also highlight the fun applications that have been created, such as converting ramen to a face and back, and generating cats based on edges.

  • 01:20:00 In this section, the lecturer discusses the evaluation of GANs and how to check if the generated images are realistic or not. One method is human annotation, where software is built and users are asked to indicate which images are fake and which ones are real. Another method is to use a classification network like the Inception network to evaluate the images. The lecturer also mentions the Inception score and the Frechet Inception Distance as popular methods for evaluating GANs. Lastly, the lecturer reminds the students about the upcoming quizzes and programming assignment and advises them to review the BatchNorm videos.
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 4 - Adversarial Attacks / GANs
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 4 - Adversarial Attacks / GANs
  • 2019.03.21
  • www.youtube.com
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford Universityhttp://onlinehub.stanford.edu/Andrew NgAdjunct Professor, Computer ScienceKia...
 

Lecture 5 - AI + Healthcare




Stanford CS230: Deep Learning | Autumn 2018 | Lecture 5 - AI + Healthcare

The lecture provides an overview of AI applications in healthcare in this lecture. He breaks down the types of questions AI can answer, such as descriptive, diagnostic, predictive, and prescriptive. The author then presents three case studies from his lab that demonstrate the application of AI to different healthcare problems. One example is the detection of serious heart arrhythmias, which experts might have misdiagnosed but could be caught by a machine. Another example is using convolutional neural networks to identify abnormalities from knee MR exams, specifically identifying the probability of an ACL tear and a meniscal tear. Finally, the speaker discusses issues related to data distribution and data augmentation in healthcare AI.

The second part covers various topics related to the implementation of deep learning in healthcare applications. The importance of data augmentation is discussed, as demonstrated by a company's solution to speech recognition issues in self-driving cars caused by people talking to the virtual assistant while looking backwards. Hyperparameters involved in transfer learning for healthcare applications, such as deciding how many layers to add and which ones to freeze, are also discussed. The lecture then moves on to image analysis, where the importance of adding boundaries to labeled datasets is highlighted. The advantages and differences between object detection and segmentation in medical image analysis are discussed, and the topic of binary classification for medical images labeled with either zero or one is introduced. The lecture concludes by discussing the importance of data in deep learning and upcoming assessments for the course.

  • 00:00:00 In this section of the video the lecture provides an overview of AI applications in healthcare. He breaks down the types of questions AI can answer, such as descriptive, diagnostic, predictive, and prescriptive. He also discusses the paradigm shift of deep learning and the potential for AI to automate the job of the machine-learning engineer. Rajpurkar then presents three case studies from his lab that demonstrate the application of AI to different healthcare problems.

  • 00:05:00 In this section, the speaker discusses the problem of detecting arrhythmias using medical imaging. Arrhythmias are a significant problem affecting millions of individuals, and detecting them through ECG tests can be challenging due to the subtle differences between heart rhythms. The speaker highlights the amount of data generated in two weeks from monitoring patients using recent devices, such as the Zio Patch, which can make automated interpretation necessary. However, detecting arrhythmias using automated methods comes with challenges such as the limited availability of several electrodes and the subtle differences between heart rhythms. To overcome these challenges, the speaker proposes using deep learning, which can change traditional approaches to feature engineering and classification.

  • 00:10:00 In this section, the speaker discusses using a deep neural network with a 1D convolutional neural network architecture that is 34 layers deep, to map heart rhythms (labeled A, B, and C) from input to output. The network used was a residual network with shortcuts that help minimize the distance from the error signal to each of the layers, and it was combined with a bigger database that was 600 times bigger than the previous largest data set. This new database allows for the algorithm to surpass cardiologists on the F1 metrics precision and recall, with the biggest mistake being distinguishing two rhythms that look very similar but have no difference in treatment, and even finding a costing mistake which the experts missed.

  • 00:15:00 In this section, the lecturer discusses the use of automation in healthcare and how deep learning and machine learning allow for continuous patient monitoring, advancing scientific understanding of risk factors, and potential medical breakthroughs. One example is the detection of serious heart arrhythmias, which experts might have misdiagnosed, but could be caught by a machine. The lecturer also discusses the detection of pneumonia with chest X-rays, highlighting the usefulness of automatic detection, especially in children where pneumonia has a high global burden.

  • 00:20:00 In this section, the speaker discusses the use of a 2D convolutional neural network that has been pre-trained on ImageNet to take an input image of a patient's chest X-ray and output a binary label indicating the presence or absence of pneumonia. The dataset used was a large dataset of 100,000 chest X-rays released by the NIH, with each X-ray annotated with up to 14 different pathologies. An evaluation was done to determine if the model was better than radiologists or on par with them by assessing if they agreed with other experts similarly. The F1-score was computed once for each expert and the model, and it was shown that the model performed better than the average radiologist in this task. The results were also better than the previous state-of-the-art on all 14 pathologies.

  • 00:25:00 In this section, the speaker discusses the challenges of diagnosing patients without access to their clinical histories and how deep learning algorithms can be trained on radiology reports which have access to more information. The goal is to identify potential pathologies from a set of symptoms seen on a new patient's chest x-ray. Model interpretation is essential in informing clinicians about the algorithm's decision-making process, and they use class activation maps to generate heat-maps that highlight areas of an image with pathologies. The approach can improve healthcare delivery by prioritizing workflow, especially in the developed world, and increase medical imaging expertise globally, where two-thirds of the population lacks access to diagnostics.

  • 00:30:00 In this section, the lecturer demonstrates a prototype app that allows users to upload X-ray images, which the model then diagnoses. The model is trained on 14 pathologies and is able to identify cardiomegaly, the enlargement of the heart. The lecturer is excited about the ability of the algorithm to generalize to populations beyond the ones it was trained on, as demonstrated by the successful diagnosis of an image downloaded from the internet. Additionally, the lecturer discusses a case study on MR images of the knee, where the goal was to identify knee abnormalities. The 3D problem allows for viewing the knee from different angles, which is essential for radiologists in making diagnoses.

  • 00:35:00 In this section, the speaker discusses using convolutional neural networks to identify abnormalities from knee MR exams, specifically identifying the probability of an ACL tear and a meniscal tear. The speaker trained nine convolutional networks for every view-pathology pair and then combined them using logistic regression. They tested the model on 120 exams and found that it did well in identifying abnormalities. The speaker also discusses the importance of being able to generalize models to work with datasets from different institutions and countries. The issue of models working together with experts in different fields, such as radiologists, to boost performance is also mentioned.

  • 00:40:00 In this section of the lecture, the speaker discusses a study on the efficacy of radiologists using an AI model to detect ACL tears. The study found that using the model alongside the radiologists increased the performance and specificity of ACL tear detection. However, the concern of automation bias arises, and the speaker addresses potential solutions, such as passing in exams with flipped answers to alert the radiologists if they are relying too much on the model. The speaker also shares two opportunities for students to get involved with AI and healthcare, including working with the MURA dataset and participating in the AI for Healthcare Bootcamp.

  • 00:45:00 In this section, the speaker discusses the applications and potential compensation for medical experts in the development and implementation of AI models in healthcare. While there is much work being done on the topic, there is no straightforward solution to the ethical concerns surrounding the potential impact on medical professionals' livelihoods. The speaker also addresses a question about the limitations of AI models in detecting certain pathologies and the importance of conveying these limitations to users. The section concludes with a case study on using deep learning to segment microscopic images of skin cells to detect diseases.

  • 00:50:00 In this section, the speaker discusses segmenting medical images and dividing the dataset into train, dev, and test sets. The images are binary segmented into pixels that correspond to cell or no cell. The audience is asked to discuss and provide strategies for splitting the data from three different microscopes - A, B, and C, with the data divided as 50% for A, 25% for B, and 25% for C. The consensus is to split the data into 95-5 for train and dev-test, with C images in the dev and test sets, and C images also included in 90% of the training data.

  • 00:55:00 In this section, the speaker discusses issues related to data distribution and data augmentation in healthcare AI. He emphasizes the importance of ensuring that the distribution of training data matches that of the real-world application, and suggests augmentation techniques such as rotation, zoom, blur, and symmetry. The speaker also warns of cases where data augmentation can hurt rather than help the model, such as in character recognition where symmetry flips can lead to mislabeling.

  • 01:00:00 In this section, the importance of data augmentation is discussed with an example of a company working on self-driving cars and virtual assistants in cars. They noticed that the speech recognition system was not working well when the car was going backwards, and discovered that people were talking to the virtual assistant with their hand on the passenger seat looking back. Using smart data augmentation, they were able to change the voices of the data to look like they were used by someone who was talking to the back of the car, which solved the problem. Additionally, topics such as hyperparameters involved in transfer learning are discussed.

  • 01:05:00 In this section, the speaker discusses the hyperparameters involved in transfer learning for healthcare applications using deep learning. They focus on hyperparameters such as number of layers, size of added layers, and the decision of which layers to freeze during training. The speaker explains how to choose which layers to keep from a pre-trained network and how many layers to add on to create a new network for segmentation. Additionally, they discuss that it is important to decide how much of the pre-trained layers to freeze during retraining for a small dataset.

  • 01:10:00 In this section, the instructor shows an image of an output produced by an algorithm, which doesn't match with what the doctor desires. The image has cells that cannot be separated, making it hard for the doctor to interpret it. The solution to this problem is to add boundaries to the labeled dataset. The datasets can be relabeled, taking into account the presence of boundaries. When the model still doesn't perform well, the loss function's weighting is adjusted, meaning that the model is trained to focus on boundaries. Coefficients can be attributed to each value in the loss function to tell the model how to proceed in case it misses boundaries. Relabeling the dataset can be done manually where you draw lines, and the area within the lines will be treated as the cell, and the boundary will be treated as the line.

  • 01:15:00 In this section, the lecture discusses the advantages and differences between object detection and segmentation in medical image analysis. While object detection may work better for faster analysis, segmentation is more precise in separating cells. The lecture then moves on to discuss binary classification for medical images that are labeled with either zero or one, indicating the presence or absence of cancer cells. The speaker recommends using gradient values to interpret the network's prediction after achieving a 99% accuracy rate. It is then questioned whether it is possible for a network to achieve higher accuracy than a doctor, to which the answer is yes due to differences in experience and perception.

  • 01:20:00 In this section, the instructors discuss base error and human level performance in healthcare AI models. They mention that the accuracy of a group of doctors who labeled the dataset has to be taken into account, as it might surpass that of a single doctor. The pipeline for autonomous driving is also discussed, and it is suggested that isolating each component and checking their performance can help identify where the problem lies. Additionally, the advantages of a pipeline approach are discussed, including that data collection can be easier to obtain for each individual step than for the entire end-to-end system.

  • 01:25:00 In this section, the instructor discusses the importance of data in deep learning and how the choice of problem to work on can depend on what data is easily accessible. He then introduces the topic of convolutional neural networks and mentions that the upcoming modules will focus heavily on image analysis. The instructor reminds students of the upcoming quiz, programming assignments, and midterm, which will cover everything up to the current week's videos.
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 5 - AI + Healthcare
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 5 - AI + Healthcare
  • 2019.03.21
  • www.youtube.com
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford Universityhttp://onlinehub.stanford.edu/Andrew NgAdjunct Professor, Computer ScienceKia...
 

Lecture 6 - Deep Learning Project Strategy




Stanford CS230: Deep Learning | Autumn 2018 | Lecture 6 - Deep Learning Project Strategy

In this video, the speaker discusses the importance of choosing a good metric to measure the success of a machine learning project. The metric chosen should reflect the problem at hand and the desired outcome. The speaker provides the examples of accuracy, precision, recall, and F1 score and explains when each one should be used. They also discuss the difference between the validation set and test set and explain why it's important to use both. Additionally, the speaker emphasizes the need for a baseline model as a point of comparison to measure the effectiveness of the learning algorithm. Finally, the speaker addresses some questions from the audience about the choice of threshold for binary classification and how to deal with class imbalance.

  • 00:00:00 In this section, the instructor introduces a project scenario of building a speech recognition system to detect a specific phrase, "Robert turn on", which can be used to turn on a lamp using voice command. The goal is to build a learning algorithm that can recognize this phrase and turn on the lamp when spoken. The instructor emphasizes the importance of being strategically sophisticated in deciding what to do next in a machine learning project to make it more efficient and drive it forward quickly. The lecture will be interactive, and students are encouraged to sit with someone they do not work with traditionally.

  • 00:05:00 In this section, the instructor asks the audience to imagine themselves as a startup CEO with the task of building a learning algorithm to detect a specific phrase. He emphasizes the importance of reading existing literature before embarking on a new project and provides tips on how to read research papers efficiently. He advises the audience to skim multiple papers at a surface level before they decide which one to read in greater detail. He also warns that not all papers make sense or are important, and thus it’s essential to filter out irrelevant information.

  • 00:10:00 In this section of the lecture, the importance of talking to experts and contacting paper authors is emphasized when trying to understand a particular topic. The speaker also discusses the process of collecting the appropriate training, development, and test datasets for a deep learning project. They suggest recording individuals saying the specific phrase to be detected, such as "Robert turn on," and using data augmentation techniques to reduce variance in the learning algorithm. The speaker stresses the significance of validating the need for data augmentation before investing time and effort into it.

  • 00:15:00 In this section, the speaker discusses an example homework problem that involves creating a trigger word detection system. The system is designed to detect when someone says a specific phrase, such as "Robert turn on," and then trigger an action, such as turning on a lamp. To collect the necessary data, the speaker suggests collecting 100 audio clips of 10 seconds each, with 25 for the development set and 0 for the test set. He explains that this process could be done quickly, estimating that one person could be recorded every minute or two in a busy area such as the Stanford cafeteria.

  • 00:20:00 In this section of the video, the lecturer discusses how to turn an audio detection problem into a binary classification problem for supervised learning. They suggest clipping out three-second audio clips of a ten-second clip, with different target labels for each clip. This method can yield thousands of training examples. The lecturer acknowledges that other methods to process sequence data exist, but this is one way of doing it. They also answer questions from the audience about sparse targets and the choice of three-second clips. Finally, they discuss a scenario where accuracy is high, but the algorithm does not detect any instances of the phrase in question.

  • 00:25:00 In this section, the speaker discusses a scenario where a learning algorithm gives 95% accuracy but no detections. They suggest that one way to improve the algorithm is to specify a dev set and evaluate metrics that are closer to the actual goal. This can be done by resampling training and dev sets to make them more proportionate in terms of positive and negative examples or by giving positive examples greater weight. Another approach could be to change the target labels to a bunch of ones, which may be a quick and dirty method but not mathematically rigorous. The speaker also addresses a question about how to rebalance data sets when deploying and refers to the need for adjusting for the bias that may be introduced.

  • 00:30:00 In this section, the speaker discusses the strategy for building learning algorithms and emphasizes that it can feel more like debugging than development. The workflow typically involves fixing a problem and then encountering a new one to solve. For instance, if the algorithm is overfitting, error analysis is needed, and more ones can be added to balance the dataset. However, the straightforward way of rebalancing can result in a lot of negative examples to be thrown away, which could have been helpful for the learning algorithm. The speaker also mentioned the metrics to measure a system's efficiency, such as the chance of waking up or turning on the lamp and the randomness of it turning on by itself.

  • 00:35:00 In this section, the speaker discusses data augmentation for audio and suggests three possible ways to collect background noise data to make a system more robust. The first method involves collecting audio samples of background sounds inside people's homes with their permission to be added to the audio clips to simulate what it would sound like in the user's home. The second method involves downloading 10-hour long audio clips of rain or cars from Creative Commons-licensed content online, while the third option is to use Amazon Mechanical Turk to get people from all around the world to provide audio samples.

  • 00:40:00 In this section of the video, the speaker asks the audience to estimate how long it would take to collect 10 hours of audio data at various locations around Stanford through different mechanisms. The speaker suggests that collecting data in parallel by having multiple friends with laptops can be done quickly, while downloading clips online might be more difficult as the clips may loop and thus not contribute to diversity of data. The speaker emphasizes the importance of going through such exercises in order to efficiently brainstorm ideas and determine how much time and effort they will require.

  • 00:45:00 In this section, the instructor explains the importance of being efficient and making choices based on brainstormed ideas and time estimates to build a decent trigger word detection system. The advice given is to quickly build something "dirty" and later develop data sets to further improve the system. The instructor emphasizes that the difference between a company's success/failure ultimately comes down to being efficient and making the most of the given time frame. Lastly, the instructor encourages students to fill out an anonymous survey to help improve the course.
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 6 - Deep Learning Project Strategy
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 6 - Deep Learning Project Strategy
  • 2019.04.03
  • www.youtube.com
Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford Universityhttp://onlinehub.stanford.edu/Andrew NgAdjunct Professor, Computer ScienceKia...
 

Lecture 7 - Interpretability of Neural Network




Stanford CS230: Deep Learning | Autumn 2018 | Lecture 7 - Interpretability of Neural Network

In this lecture, the lecturer introduces several methods for interpreting and visualizing neural networks, such as saliency maps, occlusion sensitivity, and class activation maps. The class activation maps are used to interpret intermediate layers of a neural network by mapping back the output to the input space to visualize which parts of the input were most discriminative in the decision-making process. The professor also discusses global average pooling as a way to maintain spatial information in a convolutional neural network and deconvolution as a way to up-sample the height and width of images for tasks like image segmentation. Additionally, the lecture explores the assumption of orthogonality in convolutional filters and how sub-pixel convolution can be used for reconstruction in visualization applications.

The lecture covers various methods for interpreting and visualizing neural networks, including sub-pixel convolution, 2D deconvolution, upsampling, unpooling, and the use of tools such as the DeepViz toolbox and Deep Dream algorithm. The speaker explains how visualizing filters in the first layer of a network can facilitate interpretation, but as we go deeper, the network becomes harder to understand. By examining activations in different layers, the speaker shows how certain neurons respond to specific features. While there are limitations to interpreting neural networks, visualization techniques can provide insight and potential applications such as segmentation, reconstruction, and adversarial network generation.

  • 00:00:00 In this section, the speaker introduces the idea of interpreting neural networks, as opposed to just using trial and error to improve them. They go on to introduce three methods for interpreting neural networks: saliency maps, occlusion sensitivity, and class activation maps. These methods help understand the decision-making process of the network by mapping back the input space to check which part of the input was discriminative for a certain output. The speaker then goes on to explain how they will delve even further into intermediate layers and how they will use methods such as gradient ascent class model visualization, dataset search, and deconvolution to understand the network better. The goal is to provide a scientific method for improving neural networks as opposed to just relying on trial and error.

  • 00:05:00 In this section, the lecturer discusses interpretability of neural networks and the use of saliency maps to visualize what the network is looking at. They explain that instead of using the probabilities of the softmax layer, it is better to use the scores pre-softmax in order to identify which pixels have the greatest influence on the network's general output. The lecturer also introduces occlusion sensitivity as a method for more precise visualization. This involves putting a gray square on the dog in the input image and propagating it through the network multiple times to create a probability map of the class dog, where the confidence of the network is indicated by different colors. By shifting the gray square, the map shows which regions of the input image are most crucial for the network to classify it as a dog.

  • 00:10:00 In this section, the lecturer discusses three different methods for interpreting and understanding neural networks. The first method involves occluding parts of the image to see where the network is looking and what it is focusing on. The lecturer demonstrates this method with images of dogs and chairs, showing how the network's confidence changes depending on which part of the image is occluded. The second method is occlusion sensitivity, where the network's confidence actually increases when certain parts of the image are removed. The third method is class activation maps, which demonstrate a network's ability to localize objects in images even when trained only on image-level labels. The lecturer explains that this localization ability is crucial for tasks like object detection and is often developed through training on classification tasks.

  • 00:15:00 In this section, the instructor demonstrates how to use global average pooling instead of flattened plus fully connected in a convolutional neural network (CNN) to maintain spatial information, which is useful for visualizing what the network is looking at. After obtaining a volume with six feature maps, global average pooling is applied to convert it into a vector of six values, which are then fed into a fully connected layer with softmax activation to obtain probabilities. By looking at the weights of the fully connected layer, it is possible to figure out how much each feature map contributes to the output, and a weighted sum of all these feature maps can reveal what the network is looking at in the input image.

  • 00:20:00 In this section, the speaker discusses class activation maps and how they depend on the class being analyzed in the neural network. By examining the edges between the first activation and the previous layer, the speaker explains that the weights will differ depending on the class being analyzed. By summing all of the feature maps together, different results are obtained. The speaker then discusses how class activation maps can be visualized with a network by changing the last few layers, and how this does require some fine tuning. The speaker also discusses how the global average pooling process, which involves a normalization of 116, does not kill the spatial information because the feature maps are known, and therefore can be mapped back exactly.

  • 00:25:00 In this section, the speaker explains how class activation maps work to interpret intermediate layers of a neural network. This method maps back the output to the input space, allowing users to visualize which parts of the input were most discriminative in the decision-making process. Through gradient ascent, an iterative process that maximizes the score of the desired output, the speaker provides a demonstration of how to use this method to find the image that represents what the network thinks a dog looks like. The speaker says that while this method is an effective way to interpret image data, other methods like attention models are used to interpret non-image data.

  • 00:30:00 In this section of the lecture, the professor discusses different techniques for visualizing what a neural network is seeing. He shows examples of how pushing certain pixel values can lead to a higher score for a particular class, and how regularization, such as L2 or Gaussian blurring, can improve the quality of visualizations. The professor also introduces the idea of class model visualization, where an objective function is used to maximize the score of a particular class, and how it can be used to validate that the network is looking at the right thing. Additionally, the professor talks about how data-set search can be used to understand what a particular activation in the middle of the network is thinking, by selecting a feature map and running a lot of data through the network to see which data points have the maximum activation of that feature map.

  • 00:35:00 In this section, the lecturer explains how different feature maps in a convolutional neural network are activated by different parts of an image. The lecturer presents examples of a feature map that detects shirts and another that detects edges. The lecturer then explains that the activations of an image in the network only see a subpart of the input image, and as the network goes deeper, each layer's activation looks at a larger part of the image. The lecturer also explains how deconvolution networks can be used to output images based on a code input, and how this method can be more practical than using a fully connected layer with many neurons.

  • 00:40:00 In this section, the speaker explains the use of deconvolution in neural networks. Deconvolution can up-sample the height and width of images, making it useful for tasks such as image segmentation. The speaker also discusses the gradient ascent method and how to reconstruct activations in the input space through unpooling, un-ReLU and deconvolution. The speaker then proceeds to define deconvolution as a matrix vector mathematical operation and gives an example of a 1D convolution with padding.

  • 00:45:00 In this section of the lecture, the professor is discussing the mathematical operation between a matrix and a vector. He gives an example of a convolutional layer with one filter that has a size of four and a stride of two. The output size is computed using a formula, which is nx-f+2p/stride. He then explains how to define this convolution as a mathematical operation between a matrix and a vector, by writing a system of equations and finding the shape of the matrix. The resulting matrix is filled in according to the system of equations, and the vector of activations is multiplied by the matrix.

  • 00:50:00 In this section of the lecture, the instructor explains how the convolution operation can be represented as a simple matrix times a vector. The matrix consists of weights and their placement in the matrix is dictated by the stride and window size. By framing convolution as a matrix operation, we can then invert the matrix to perform deconvolution and reconstruct the original input. However, this approach assumes that the weight matrix is invertible and orthogonal, which is not always true in practice. The assumption of orthogonality is useful in cases where the convolutional filter is an edge detector.

  • 00:55:00 In this section of the lecture, the professor introduces a method for generating X from Y using the assumption that the reconstruction will be useful even if it is not always true. They demonstrate the process using illustrations and a Menti code, showcasing how a sub-pixel convolution can be used to perform the same operation with a strike going from left to right instead of up to down. The technique involves cropping and padding the input to get the desired output. The professor notes that this type of convolution is often used for reconstruction in visualization applications.

  • 01:00:00 In this section, the lecturer explains the concept of sub-pixel convolution, which involves inserting zeros into a vector Y to allow for more efficient computation of deconvolution. By flipping weights, dividing stride by two, and inserting zeros, the deconvolution process becomes essentially equivalent to convolution. This process can be extended to two-dimensional convolution, and overall provides a better understanding of the mathematical operation between a matrix and a vector for convolution.

  • 01:05:00 In this section, the speaker delves into the interpretation of 2D deconvolution. The intention behind the deconvolution is to get a five-by-five input, which is the reconstructed x. To do this, the speaker demonstrates that a filter of size two-by-two is applied to forward propagating inputs with stride equal to two in a conv layer. Then, the deconvolution technique is applied to get the reconstructed image. The lecture explains that the deconvolution process involves taking the filter and multiplying all the weights by y11, shifting this by a stride of one and repeating the same process for all entries. The speaker concludes by noting that the process is somewhat complicated; however, there is no need to worry if the concept of deconvolution is not well understood.

  • 01:10:00 In this section of the lecture, the professor explains the upsampling process for an image in a visual way. He explains that in order to reconstruct an image, the weights from the ConvNet should be used if possible. He then shows a visual representation of the upsampling process starting with a 4x4 image, inserting zeros and padding it to a 9x9 image before using a filter to convolve over the image and perform convolution up as it goes. He also briefly discusses how to unpool and unReLU, stating that max pool is not mathematically invertible, but the process can be approximated through spreading and caching switches for maximum values.

  • 01:15:00 In this section, the concept of unpooling and maxpooling in neural networks is explained, along with the use of switches and filters to reconstruct the original input. The ReLU activation function is also discussed, and the concept of ReLU backward is introduced. The use of ReLU DeconvNet is explained as a method for unbiased reconstruction that does not depend on the forward propagation. The approach is described as a hack and is not always scientifically viable, but it is useful in visualizing and interpreting the neural network.

  • 01:20:00 In this section of the lecture, the speaker explains how to visualize and understand what is happening inside neural networks by finding out what each activation corresponds to. The visualization technique involves choosing an activation, finding the maximum activation, setting all others to zero, and then reconstructing the image. The speaker discusses how filters in the first layer of the network can be interpretable due to the fact that the weights are directly multiplying the pixels. However, as we go deeper into the network, the filters become harder to interpret. The speaker also goes on to explain how the deeper we go, the more complexity we see, and provides examples of different filters and the types of images that activate them.

  • 01:25:00 In this section of the lecture, the speaker demonstrates the use of the DeepViz toolbox to investigate the interpretability of neural networks. By examining the activations of neurons in different layers of a convolutional network, the speaker shows how certain neurons fire in response to specific features, such as faces or wrinkles. The speaker also mentions the optional use of the Deep Dream technique to generate images by setting the gradient to be equal to the activations of a specific layer, allowing for further exploration of neural network behavior.

  • 01:30:00 In this section, the speaker demonstrates the Deep Dream algorithm, which generates images by backpropagating the activations of a neural network to the input layer and updating the pixels. The result is a variety of surreal images with animals and other objects morphed together. The speaker also discusses the limitations of interpreting neural networks and the ways in which visualization techniques, such as class activation maps and deconvolutions, can be used to understand how the network sees the world and detect dead neurons. Additionally, the speaker highlights the potential applications of these visualizations, including segmentation, reconstruction, and adversarial network generation.
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 7 - Interpretability of Neural Network
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 7 - Interpretability of Neural Network
  • 2019.04.03
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Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford Universityhttp://onlinehub.stanford.edu/Andrew NgAdjunct Professor, Computer ScienceKia...
 

Lecture 8 - Career Advice / Reading Research Papers




Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers

In this lecture, Professor Andrew Ng provides advice on how to efficiently read research papers and keep up with the rapidly evolving field of deep learning. He emphasizes the importance of summarizing the work in the introductory and concluding sections, as well as paying attention to the figures and tables. Ng also shares career advice, recommending that job candidates have both broad and deep knowledge in multiple AI and machine learning areas, and to focus on working with individuals rather than big brand names in order to maximize growth opportunities. He suggests consistency in reading papers and building both horizontal and vertical skills through courses and projects for a strong foundation in machine learning.

  • 00:00:00 In this section of the lecture, the speaker shares advice on how to read research papers efficiently, particularly in the rapidly evolving field of deep learning. He suggests compiling a list of papers and resources, including research papers posted on arXiv, Medium posts, and occasional GitHub posts. He then recommends skimming through the papers and quickly understanding each of them, skipping over papers that don't make sense or are not helpful. He suggests spending more time on seminal papers and using the citations to find additional papers on the topic.

  • 00:05:00 In this section, the lecturer provides guidelines for reading research papers in order to increase one's understanding of a particular topic. He suggests that reading 15 to 20 papers will give a basic understanding of an area, while reading 50 to 100 papers will lead to a very good understanding. Additionally, he provides advice on how to read one paper, suggesting that multiple passes should be taken through the paper, with a focus on reading the title, abstract, and figures during the first pass. The lecturer emphasizes the importance of summarizing the work in the introductory and concluding sections, as these are often where authors make a clear case for the significance of their work.

  • 00:10:00 In this section of the lecture, the speaker gives advice on how to efficiently read research papers. He suggests starting with the abstract, introduction, and conclusion of the paper to get a clear understanding of what it's about. He also advises to skim the related work section, which can often be difficult to understand if you're not already familiar with the literature. The speaker recommends reading the whole paper but skipping over parts that don't make sense, as it's not uncommon for papers to have unimportant sections included. Lastly, he offers a set of questions for readers to try and answer in order to solidify their understanding of the paper, including what the authors were trying to accomplish and what key elements can be applied.

  • 00:15:00 In this section of the lecture, the professor encourages students to read research papers and recommends starting with the English text before delving into the math. He assigns a paper called "Densely Connected Convolutional Neural Networks" and suggests that students take seven minutes to read it before discussing it with their classmates. He also notes that with practice, students can get faster at reading and understanding research papers, including understanding the common formats used to describe network architecture. The professor emphasizes that one can learn more quickly by focusing on the main concepts presented in the figures and tables of the paper.

  • 00:20:00 In this section, Professor Andrew Ng gives advice on how to keep up with and understand deep learning research. He suggests doing web searches and looking for blog posts on important papers, checking Twitter and the ML Subreddit, and following researchers who frequently share papers online. Ng also recommends forming a community with colleagues or classmates to share interesting papers, and to re-derive math from detailed notes in order to deeply understand the algorithm. Ng emphasizes that time spent per paper can vary depending on experience level and difficulty, but spending more time can lead to a richer understanding of deep learning concepts.

  • 00:25:00 In this section, the instructor advises students to re-derive machine learning algorithms from scratch to ensure a deep understanding, as it allows for the ability to generalize and derive new algorithms. He also recommends spaced repetition over cramming when it comes to learning, and encourages students to form reading groups and collaborate with peers to continue learning and navigating a career in machine learning. He emphasizes steady learning over intense activity and provides tips on how to approach career navigation.

  • 00:30:00 In this section of the lecture, the speaker discusses how to get a job or join a PhD program in the machine learning field and emphasizes the importance of doing important work. Recruiters look for technical skills, coding ability, and meaningful work experience in machine learning. The ability to keep learning new skills and staying up to date with the field's rapid evolution is also highly valued. Successful AI and machine learning engineers are those who have learned about different areas of machine learning and have experienced working with those areas, leading to a strong understanding of how to apply machine learning algorithms in various settings.

  • 00:35:00 In this section, the lecturer discusses the "T-shaped" skills that are desirable in job candidates, which means having a broad understanding of multiple AI and machine learning areas, while having a deep understanding in at least one specific area. He emphasizes the importance of having practical experience, such as working on meaningful projects, contributing to open source or doing research to convince recruiters of the candidate's abilities. The lecturer warns against taking too many classes without gaining practical experience, attempting to jump too deep too quickly, or doing too many tiny projects with little depth.

  • 00:40:00 In this section of the lecture, Professor Ng gives advice on how to build a solid foundation in machine learning by recommending building horizontal and vertical pieces. He notes that completing 10 small projects may not impress recruiters as much as one or two great projects. To build the horizontal piece, which consists of foundational skills in AI and machine learning, he recommends taking courses, reading research papers, and joining a community. For building the vertical piece, which involves doing more relevant, deep projects, Ng advises working on things that are relevant to machine learning or AI to help grow a career in these fields. He goes on to stress the importance of having fun and taking breaks, as there is often no short-term reward for deep learning work aside from personal satisfaction.

  • 00:45:00 In this section, the lecturer discusses how consistency is key in order to improve in the field of deep learning. Reading two papers a week consistently for a year will lead to having read 100 papers, and contribute to one's improvement in the field. Moreover, great people and projects are the biggest predictors of success, and having close friends who work hard, read a lot of papers, and care about their work can influence one to do the same. In selecting a job, it is advised to focus on the team and to interact with a group of 10 to 30 people who can build one's career and improve skills.

  • 00:50:00 In this section, the speaker provides career advice for deep learning enthusiasts, urging them to focus on individuals in a company instead of its brand. The speaker highlights that one's manager and the core group they interact with will influence them the most, considering their level of hard work and willingness to teach, making personal evaluation and connections with individuals more important than the company’s brand. Example scenarios given, such as a giant company sending job offers to a small AI team, are assessed, with the focus being on individuals and how it influences one’s growth. The failure mode of ignoring individuals in favour of company branding is highlighted with a personal example of a student whose career plateaued after accepting a Java-based back-end payment job offer from a well-known company, rather than focusing on working with specific people on a small team.

  • 00:55:00 In this section, Andrew Ng advises caution when considering rotation programs that sound good in theory but may not provide clear direction or opportunity for growth within a company. He suggests seeking opportunities to work with smaller, lesser-known teams that may be doing important work in machine learning, rather than chasing after big brand names. He emphasizes the importance of prioritizing learning experiences and doing impactful work over focusing solely on prestigious brand names in the industry.

  • 01:00:00 In this section of the video, the speaker gives career advice to those in the early stages of their career. Joining a team with a great set of teammates and doing meaningful work that helps other people are recommended. However, he advises not to work for companies that produce harmful products like cigarettes. He believes that there is a lot of important work to be done in various industries, and that the world needs people to work on different things. He suggests that the next wave of machine learning is not only for tech companies, but also to look at all the traditional industries that do not have technology implemented.
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers
  • 2019.04.03
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Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford Universityhttp://onlinehub.stanford.edu/Andrew NgAdjunct Professor, Computer ScienceKia...
 

Lecture 9 - Deep Reinforcement Learning




Stanford CS230: Deep Learning | Autumn 2018 | Lecture 9 - Deep Reinforcement Learning

The lecture introduces deep reinforcement learning, which combines deep learning and reinforcement learning. Reinforcement learning is used to make good sequences of decisions in situations with delayed labels, and it is applied in different fields such as robotics, games, and advertisements. Deep reinforcement learning replaces the Q-table with a Q-function that is a neural network. The lecturer discusses the challenges of applying deep reinforcement learning but describes a technique for creating a target value for Q-scores based on the Bellman equation to train the network. The lecture also discusses the importance of experience replay in training deep reinforcement learning and the trade-off between exploitation and exploration in RL algorithms. The practical application of deep reinforcement learning to the game Breakout is also discussed.

The lecture discusses various topics related to deep reinforcement learning (DRL). The exploration-exploitation trade-off in DRL is discussed, and a solution using a hyper-parameter is proposed that decides the probability of exploration. The importance of human knowledge in DRL and how it can augment algorithmic decision-making processes is explored. The lecture also covers policy gradients, different methods for their implementation, and overfitting prevention. Additionally, the challenges in sparse reward environments are highlighted, and a solution from a recent paper called "Unifying the Count-based Metas for Exploration" is briefly discussed. Lastly, the lecture briefly mentions the YOLO and YOLO v2 papers from Redmon et al. regarding object detection.

  • 00:00:00 In this section, the speaker introduces the idea of deep reinforcement learning which is the combination of deep learning and another area of AI which is reinforcement learning. The speaker explains that deep neural networks are great at function approximation and can be applied to many different fields that require function approximators, and reinforcement learning is one of these examples. The speaker motivates the idea of reinforcement learning with examples such as AlphaGo and Google's DeepMind paper where they used deep learning to train an agent to beat human-level performance in various games, mainly Atari games. The speaker also explains that reinforcement learning is important because it allows agents to have a long-term strategy in complex games like Go, which is much larger than a chessboard.

  • 00:05:00 In this section of the video, the professor challenges students to consider how to build an agent that could learn to win at the game of Go using deep learning. One possible data set would be a input-output pairing of the game board and a probability of victory for that position, but this is difficult because it is hard to represent the probability of winning in a given board position. Another option would be to watch professional players' moves and record these as the data inputs and outputs, building a data set of moves that professional players made in the past. However, this is also difficult because there are too many states in the game for an accurate representation, and the ground truth is likely to be wrong as different professional players have different strategies. There is also a risk that the algorithm would not generalize because it is a question of strategy rather than simple pattern recognition.

  • 00:10:00 In this section, the lecturer introduces reinforcement learning (RL), which is a method for automatically learning to make good sequences of decisions. RL is used in situations where delayed labels, such as a probability of victory in a game, are present. RL is applied in various fields such as robotics, games, and advertisements. To illustrate how RL works, the lecturer introduces a game with five states and explains how the long-term return is defined in this game. The goal of the game is to maximize the reward over the long-term by moving through the states and making decisions based on the available rewards.

  • 00:15:00 In this section, the concept of long-term return and the use of discounted return for Q-learning in reinforcement learning are discussed. The discounted return takes into account the importance of time in decision-making and helps mitigate the issue of convergence that might occur with a non-discounted return. The goal of Q-learning is to learn the optimal action in each state by storing a Q table matrix that represents the score for each action in every state. By using the Q table scores, an agent can determine the maximum value and corresponding action in a given state to make decisions quickly. The process of building a Q table through a tree diagram was also explained.

  • 00:20:00 In this section, the professor explains the iterative algorithm of Q-learning, using a matrix that tells the action to take in every state. To compute the long-term discounted reward for each state, they use the Bellman equation, which consists of the immediate reward plus the discount times the maximum possible future reward. The iterative algorithm should converge at some point, and the Q-function should follow the optimal Bellman equation. The professor emphasizes the importance of the Bellman equation in understanding Q-learning.

  • 00:25:00 In this section, the speaker talks about the vocabulary of reinforcement learning which includes the environment, the agent, state, action, policy, reward, total return, discount factor, Q-table, and the Bellman equation. The Q-table is the matrix of entries representing how good it is to take action A in state S, and the policy is the decision-making function that tells us what's the best strategy to apply in a state. The number of states can be too large, making Q-table solution impractical. Deep learning comes into reinforcement learning by replacing the Q-table with a Q-function which is a neural network. However, the dynamic changes in Q-scores make the training of the network different from a classic supervised learning setting.

  • 00:30:00 In this section of the lecture, the professor discusses the challenges that arise when applying deep reinforcement learning, as it differs significantly from supervised learning. One of the main issues is the lack of labels, since the Q-scores are dynamic and constantly changing. To address this, the professor describes a technique for creating a target value or label for the Q-scores based on the Bellman equation. Using this proxy as the label, the network can be trained to get closer to the optimal Q-function through iterative updates that hopefully lead to convergence.

  • 00:35:00 In this section, the concept of the Bellman equation and its use in backpropagation in deep reinforcement learning is discussed. The Bellman equation is used to compute values that are closer to the optimal values one is trying to get to in terms of rewards. The Q function is generated and compared to the Bellman equation to determine the best Q function. However, there is a potential for divergence in the algorithm and the convergence of the algorithm is proven in a paper by Francisco Melo. The implementation of the DQN algorithm is explained through pseudocode which involves initializing Q-network parameters, looping over episodes, computing the target value through the Bellman equation, and backpropagation using a fixed Q target network.

  • 00:40:00 In this section, the video discusses the practical application of a Deep Q-Network to the game Breakout. The goal of Breakout is to destroy all the bricks without letting the ball pass the bottom line. After training using Q-learning, the agent figured out a trick to finish the game quickly by digging a tunnel to get to the other side of the bricks. The network figured out this strategy on its own without human supervision. The input of the Q-network is a feature representation that includes the position of the ball, the paddle, and the bricks. However, to get the entire information the pixels should be used. The output of the network is three Q values representing the action of going left, going right, or staying idle in a specific state.

  • 00:45:00 In this section, the speaker discusses various preprocessing techniques to help set up the deep Q network architecture for deep reinforcement learning, specifically in working with images. The first technique involves taking successive frames in order to provide additional information to the network, while other preprocessing techniques include reducing the size of inputs, grayscale conversion for image compression, and the removal of unimportant pixels such as scores in certain games. The speaker warns of the dangers of losing important information when reducing to grayscale though, and explains the deep Q network architecture in detail, saying that convolutional neural networks are utilized due to the input being images. Finally, the speaker explains the need to keep track of a terminal state in order to ensure proper loop termination, which is important to the y function.

  • 00:50:00 In this section, the lecturer explains the importance of experience replay in reinforcement learning, which allows for training on past experiences rather than just what is currently being explored. Since reinforcement learning only trains on what it explores, it may never come across certain state transitions again, making past experiences invaluable for training. Experience replay creates a replay memory where past experiences can be stored, and during training, the algorithm can sample from the replay memory in addition to exploring new state transitions. This allows for past experiences to be used multiple times in training, which can be crucial in learning important data points.

  • 00:55:00 In this section of the lecture, the speaker discusses the advantages of experience replay in deep reinforcement learning. Firstly, it allows for data to be used many times, rather than just once, which improves data efficiency. Secondly, experience replay de-correlates experiences, preventing the network from being biased towards predicting one action repeatedly. Finally, it allows for computation and memory to be traded against exploration, which is costly. The speaker also talks about the trade-off between exploitation and exploration in the RL algorithm, and suggests a way to incentivize exploration by not always taking the best action.

  • 01:00:00 In this section, the instructor and the students discuss the exploration-exploitation trade-off problem in reinforcement learning and offer a solution using a hyper-parameter that decides with what probability the agent should explore instead of exploiting. They explain why exploration is crucial and add lines to the pseudocode for epsilon-greedy exploration in the replay memory. They emphasize that the main advantage of using deep learning in reinforcement learning is its ability to approximate functions well. Finally, they briefly touch on the topic of human knowledge in reinforcement learning and why it is essential to evaluate the performance of the algorithm.

  • 01:05:00 In this section of the lecture, the professor explains how human knowledge plays a significant role in Deep Reinforcement Learning (DRL). Humans can efficiently and instinctively interpret contextual cues, e.g. humans know that a key unlocks a door, and that understanding can significantly augment the algorithmic decision-making process. The difficulty comes in training algorithms with limited contextual information, such as the notoriously challenging Montezuma's Revenge game, a feat that DeepMind achieved by implementing tree search and deep-learning algorithms. The lecture briefly touches on the Alpha Go game and on how combined tree search and value networks can improve algorithmic decision-making processes.

  • 01:10:00 In this section, the lecturer introduces policy gradients, which is a whole different class of algorithm from DQN that optimizes for mapping from state to action (policy) directly. The lecturer explains that in policy gradients, the focus is on the policy itself, rather than the Q-value, and that the policy network is updated using the gradient of the policy, as opposed to the Q-function update in DQN. Through various videos, the lecturer explains the different policy gradient methods like Proximal Policy Optimization (PPO) and Competitive Self-Play, and highlights technical points on overfitting to the actual agent in front of you, suggesting the need to alternate between different versions of the agent to avoid overfitting. Finally, the lecturer explains how meta-learning trains on a distribution of similar tasks to enable learning specific tasks with minimal gradient steps.

  • 01:15:00 In this section of the lecture, the speaker discusses the exploration-exploitation dilemma and how it can be a challenge, especially when the reward is sparse. He talks about a recent paper called "Unifying the Count-based Metas for Exploration" that introduces the idea of keeping counts on how many times a state has been visited and giving an intrinsic reward to the agent for visiting states with fewer counts. This encourages the agent to explore and look more, leading it to discover different rooms in the game. The speaker also briefly discusses imitation learning and how it can help when defining rewards is hard.

  • 01:20:00 In this section, the speaker briefly mentions that they covered the YOLO and YOLO v2 papers from Redmon et al. regarding object detection. No further information is given.
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 9 - Deep Reinforcement Learning
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 9 - Deep Reinforcement Learning
  • 2019.04.03
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Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer - Stanford Universityhttp://onlinehub.stanford.edu/Andrew NgAdjunct Professor, Computer ScienceKia...