Machine Learning and Neural Networks - page 8
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Toward Singularity - Neuroscience Inspiring AI
Toward Singularity - Neuroscience Inspiring AIThis 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.
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
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.
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.
Lecture 3 - Full-Cycle Deep Learning Projects
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 3 - Full-Cycle Deep Learning ProjectsIn 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.
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.
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.
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.
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.
Lecture 7 - Interpretability of Neural Network
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 7 - Interpretability of Neural NetworkIn 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.
Lecture 8 - Career Advice / Reading Research Papers
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research PapersIn 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.
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.