Machine Learning and Neural Networks - page 32

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Lesson 1: Practical Deep Learning for Coders 2022

Lesson 1: Practical Deep Learning for Coders 2022

In this YouTube video "Lesson 1: Practical Deep Learning for Coders 2022", the instructor introduces the course, highlighting the rapid pace of change in deep learning, and demonstrates the ease of creating a "bird or not bird" system using Python. The course aims to show people how to build and deploy models first, rather than starting with a review of linear algebra and calculus, and will cover a range of deep learning models, including image-based algorithms that can classify sounds or mouse movements. The instructor emphasizes the importance of data block creation, understanding feature detectors, and using pre-trained models to reduce coding requirements. The course also covers segmentation and tabular analysis, with fast.ai providing best practices that help reduce coding and improve results.

The video provides an introduction to deep learning and its applications in various fields. The presenter discusses the basics of machine learning, including the process of model training and the importance of calculating loss to update the model's weight for better performance. The lesson covers two models: tabular learning and collaborative filtering. The presenter also highlights the usefulness of Jupyter notebooks in creating and sharing code, including examples of past student projects that have led to new startups, scientific papers, and job offers. The main takeaway is that aspiring deep learners should experiment and share their work with the community to gain valuable feedback and experience.

• 00:00:00 In this section, the instructor introduces the first lesson of Practical Deep Learning for Coders version 5, highlighting how much has changed since the course was last updated. He uses a humorous XKCD comic from the end of 2015 as an example of how quickly things are evolving in the field of deep learning. Subsequently, he demonstrates the creation of a "bird or not bird" system using Python, which involves downloading and resizing images of birds and forests, creating a data block using fast.ai, and displaying some of the downloaded images. The instructor emphasizes that the course will provide a lot more details and that the goal of this section is to give a quick high-level overview.
  
  
• 00:05:00 In this section, the presenter shows a demonstration of how easy it is to create a computer vision model and identify whether an image contains a bird or forest with just 200 pictures of each. What used to be almost impossible has now become easily accessible with deep learning, and the presenter shares the example of DALLꞏEꞏ2, an algorithm that generates new pictures from plain text. These recent advancements in deep learning are a testament to how fast this field moves, and the presenter notes that it is accessible without requiring a lot of code, math, or anything more than a laptop computer.
  
  
• 00:10:00 In this section, the speaker discusses the capabilities of deep learning models and how they are performing tasks that were once believed to be impossible for computers. They mention how deep learning is being used in art and language models, such as Google's pathways language model that can explain the answer to a joke. The speaker also acknowledges the need for ethical considerations in deep learning and recommends checking out a data ethics course on ethics.fast.ai. They then introduce an online version of the coloured cup system to check how the students are doing and thank Radek for creating it, who has just announced that he has landed his dream job at Nvidia AI.
  
  
• 00:15:00 In this section of the video, the instructor emphasizes the importance of context in learning, particularly in the field of deep learning. Rather than starting with a review of linear algebra and calculus, the instructor believes that people learn more effectively when given a context in place. He uses the analogy of learning sports, where one is shown a whole game of sports and then gradually puts more pieces together. This is the approach he takes in this course, where one will learn to build and deploy models first and then go into as much depth as the most sophisticated, technically detailed classes later. The instructor also discusses his credentials and background in machine learning, including the writing of the popular book "Deep Learning for Coders."
  
  
• 00:20:00 In this section, the instructor explains the historical approach of computer vision before the introduction of neural networks. He describes how previous machine learning models relied on experts to craft features that dictated how the algorithm would identify objects. The instructor contrasts this with neural networks that learn these features themselves, enabling much faster development and training of models. The instructor notes that the ability of neural networks to learn their own features and to adapt to new data are key to why deep learning has been successful in recent years.
  
  
• 00:25:00 In this section, the instructor explains the concept of feature detectors in deep learning, which are layers of neural networks that can identify and extract specific features from images without human intervention. He illustrated how these feature detectors can be combined to recognize more complex and sophisticated images. Additionally, the instructor emphasizes the versatility of image-based algorithms and how they can be used to classify sounds or even mouse movements. Lastly, he debunks the myth that deep learning requires lots of data, expensive computers, and extensive math, stating that transfer learning allows for state-of-the-art work using minimal resources.
  
  
• 00:30:00 In this section, the instructor discusses the popularity of Pytorch versus Tensorflow in the deep learning world, with Pytorch growing rapidly and surpassing Tensorflow in research repositories and among researchers. However, he notes that Pytorch can require a lot of code for relatively simple tasks, which is where the fast.ai library comes in handy. The fast.ai library is built on top of Pytorch and provides best practices that help reduce the amount of code needed and improve results. The instructor also introduces Jupyter notebook, a web-based application widely used in industry, academia, and teaching for data science, and mentions cloud servers like Kaggle that can be used to run Jupyter notebooks.
  
  
• 00:35:00 In this section of the video, the instructor introduces how to use Jupyter notebooks to code, experiment and explore with examples. He explains how to edit or copy someone else's notebook, start a virtual computer to run codes, use keyboard shortcuts, write prose or markups, and insert images to the notebook. The course also covers the latest version of fast.ai and a little amount of Python code. Through Jupyter notebooks, developers can showcase their code and make their work accessible to other people in the open-source community.
  
  
• 00:40:00 In this section, the speaker discusses the use of external libraries and introduces some of the fast.ai libraries such as "fastdownload" and "resize_images". They also explain the importance of the data block command, which is used to get the data into the model. The data block has five main parameters to specify, including the input type, output type, label type, and what items we need to train from. The speaker emphasizes that understanding the data block is crucial for deep learning practitioners as tweaking neural network architecture rarely comes up in practice, and this course is focused on practical deep learning.
  
  
• 00:45:00 In this section, the speaker explains the steps involved in creating a data block, which is critical to the success of a deep learning model. The data block is responsible for finding images to train on by using a function that retrieves all image files within a specified path, setting aside some data for testing, and resizing the images to a standard size. Creating a data block is followed by the creation of data loaders, which provide a stream of data batches that can be processed by the model. The speaker also introduces the concept of a learner, which combines the model and data and is essential for training a deep learning model.
  
  
• 00:50:00 In this section, the instructor discusses how to use pre-trained models in fast.ai for computer vision tasks using the pytorch image models (timm) library. The resnet model family is sufficient for most use cases, but there are many other models available for use. The instructor demonstrates how to fine-tune the model for a specific task, such as recognizing pictures of birds in a forest, and how to deploy the model using the predict() method. The instructor also notes that there are many other types of models besides computer vision available, including segmentation.
  
  
• 00:55:00 In this section, the speaker explains segmentation, which is used to color every pixel in an image according to what it represents. Using a tiny amount of data and minimal code, the speaker shows how a model can be trained to segment images of road scenes into different categories such as cars, fences, and buildings in just 20 seconds, with a trained model being close to perfect after 2 minutes. The speaker explains that special data loaders classes can be used for data handling, requiring even less code for data sets that occur frequently. The speaker then moves on to explain tabular analysis and how it is widely used in industries such as predicting columns of spreadsheets and data tables. By providing similar information to data blocks and using type dispatch, you can use fast.ai to automatically do the right thing for your data, regardless of the kind of data it is.
  
  
• 01:00:00 In this section, the lesson covers two types of models: tabular learning and collaborative filtering. Tabular models are used for data with no pre-trained model, where the tables of data vary widely. Collaborative Filtering is the basis for most recommendation systems and works by finding similar users based on which products they like. A collaborative filtering dataset will have a user ID, a product ID (like a movie), and a rating. The lesson goes on to show how to create collaborative filtering data loaders and discusses the differences between fine-tuning and fitting a model.
  
  
• 01:05:00 In this section, the speaker talks about the usefulness of Jupyter notebooks as a tool for creating and sharing code, including the fact that the entire fast.ai library is written in notebooks. Additionally, the speaker touches on the current state of deep learning and its applications in various fields, including NLP, computer vision, medicine, recommendation systems, playing games, and robotics. The speaker notes that deep learning has been able to break state-of-the-art results in many fields, and that it is generally good at tasks that a human can do reasonably quickly.
  
  
• 01:10:00 In this section, the presenter explains the basic idea of machine learning, starting with a normal program that has inputs and results coded with conditionals, loops, and variables. The program is then replaced with a model that contains random weights, and the model is a mathematical function that takes inputs and multiplies them together by the weights. The model is essentially useless unless the weights are carefully chosen, so it's necessary to calculate the loss, which is a number that measures the quality of the results, and update the weights to create a new set that is slightly better than the previous set. This process is critical in improving the model's performance.
  
  
• 01:15:00 In this section, the speaker explains the process of training a model in machine learning, which involves repeating a simple sequence of steps that use neural networks to produce output while replacing negatives with zeros. In theory, the model can solve any computable function if given enough time, data, and inputs, and the trained model can be integrated into any program as another piece of code that maps inputs to results. Python programmers are likely to take to the process easily, but those who are not familiar can still experiment with the Kaggle notebooks and try different things like modifying the bird or forest exercise, trying three or four categories, and sharing their work in the forums. The most important thing is to experiment and read chapter 1 of the book to be prepared for the next lesson.
  
  
• 01:20:00 In this section of the video, the instructor shares several examples of projects that past students have worked on in the "Practical Deep Learning for Coders" course, which led to new startups, scientific papers, and job offers. These projects include classifying different types of people based on where they live, creating a zucchini and cucumber classifier, accurately classifying satellite imagery into 110 different cities, and recognizing the state of buildings for disaster resilience efforts. Some students have even beaten the state-of-the-art in their respective fields, such as a sound classifier and tumor-normal sequencing. The instructor encourages current and future students to start creating projects, no matter their level of experience, and to share them with the forum for feedback and encouragement.

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Lesson 2: Practical Deep Learning for Coders 2022

Lesson 2: Practical Deep Learning for Coders 2022

This YouTube video series provides an introduction to deep learning for coders. It covers topics such as data preparation, model creation, and deploying a machine learning model.

In this video, hacker Jeremy Howard teaches people how to create their own web apps using deep learning. He covers how to set up a project in Git, how to use the hugging face space to upload a model to be trained on, natural language processing, and how to recognize text.

• 00:00:00 This lesson covers practical deep learning for coders in 2022. New, cutting-edge techniques are being taught that will help students remember material better. The course goes with the book, and quizzes are available to help students test their progress.
  
  
• 00:05:00 This video covers the basics of deep learning for coding, including how to find data, data cleaning, and putting a model into production. The next video in the series will show how to do this.
  
  
• 00:10:00 In this video, Wes discusses how to train a model in deep learning using the ddg website. He shows how to search for objects, and how to resize images.
  
  
• 00:15:00 In this video, a technique for deep learning is introduced: RandomResizedCrop. This is used to improve the accuracy of recognition of images. Data augmentation is also discussed, and it is shown that if you want to train a deep learning model for more than five or ten epochs, you should use RandomResizedCrop and "aug_transforms."
  
  
• 00:20:00 This YouTube video demonstrates how to use a classifier interpretation object to determine where a loss is high in a data set. This information can then be used to clean up the data before it is used to train a model.
  
  
• 00:25:00 The video introduces data cleaning and practical deep learning for coders 2022. It covers data preparation with tools such as GPU-accelerated data cleaning and HuggingFace Spaces, followed by using Gradio for machine learning models in production.
  
  
• 00:30:00 This video covers how to use Git to manage your code and how to use a terminal to work on code. The video also goes over how to use VS Code to edit code.
  
  
• 00:35:00 This tutorial explains how to create a deep learning model using a few different methods, including a Kaggle example and a Colab example. Once the model is created, it can be downloaded and copied into the same directory as the code.
  
  
• 00:40:00 In this lesson, the author shows how to use a trained learner to predict whether an image is a dog or cat. The learner is frozen in time, and can be loaded and unloaded easily.
  
  
• 00:45:00 This video explains how to create a Gradio interface to convert images into classifications, and how to create a Python script to do this.
  
  
• 00:50:00 In this lesson, the instructor demonstrates how to create a simple PyTorch model and upload it to Gradio. He also provides instructions on how to use fastsetup to install PyTorch and Jupyter Notebooks on a computer.
  
  
• 00:55:00 This video provides instructions for installing python and some of the necessary libraries for deep learning, including Pytorch and Numpy. The author recommends using a conda-based python distribution, such as mambaforge, instead of the system python. Finally, the author recommends installing nbdev to use Jupyter Notebook.
  
  
• 01:00:00 In this lesson, the instructor demonstrates how to use Gradio, a free platform for training deep learning models, to create a website that predicts the names of cats and dogs. While streamlit is more flexible than Gradio, both platforms are free and easy to use.
  
  
• 01:05:00 This video explains how to create deep learning models using Javascript. The tutorial includes a multi-file version and an HTML version of the same model.
  
  
• 01:10:00 This video explains how to create a basic deep learning app using only Javascript and a browser. Once the app is created, you can use FastPages to create a website for it that looks like the app.
  
  
• 01:15:00 In this video, hacker Jeremy Howard teaches people how to create their own web apps using deep learning. He first discusses how to set up a simple project in Git, and then shows how to use the hugging face space to upload a model to be trained on. Next, he discusses natural language processing, explaining how models work under the hood. Finally, he demonstrates how deep learning can be used to recognize text.

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Lesson 3: Practical Deep Learning for Coders 2022

Lesson 3: Practical Deep Learning for Coders 2022

This video provides an introduction to practical deep learning for coders. It covers the basics of matrix multiplication and gradients, and demonstrates how to use a deep learning model to predict the probability of dog and cat breeds. This video provides a brief introduction to deep learning for coders, including a discussion of how matrix multiplication can take a long time to get an intuitive feel for. The next lesson will focus on natural language processing, which is about taking text data and making predictions based on its prose.

• 00:00:00 This lesson covers matrix multiplications and gradients, and is meant for more mathematically inclined students. The course also includes a "Lesson Zero" on setting up a Linux box from scratch.
  
  
• 00:05:00 This week's video features five students who have created different projects related to deep learning. One student has created a Marvel detector, another has created a game where the computer always loses, another has created an application to predict average temperatures, another has created an art movement classifier, and lastly, a student has created a redaction detector.
  
  
• 00:10:00 This video covers practical deep learning for coders, including the use of different platforms and deep learning libraries. It covers how to train a model and deploy it to a production environment.
  
  
• 00:15:00 In this lesson, the student explains how deep learning works and how to use different deep learning models. He also shows how to use a deep learning model to predict the probability of dog and cat breeds.
  
  
• 00:20:00 In this video, a deep learning model is demonstrated. The model consists of layers, each of which contains code and parameters. The model is flexible and can be trained to recognize patterns in data.
  
  
• 00:25:00 In this lesson, the instructor shows how to create a function to fit a data set using partial application of a function. He then demonstrates how to plot the function and how to adjust the coefficients of the function to make it fit the data better.
  
  
• 00:30:00 This video explains how to improve a computer's ability to predict values using a loss function. The author demonstrates how to do this by moving sliders on a graphical slider-based model and checking the loss function to see if it gets better or worse. Pytorch can automatically calculate the gradient for you, making the process fast and easy.
  
  
• 00:35:00 In this video, the Pytorch programmer explains how to use the gradient function to adjust the coefficients of a quadratic equation. The gradient function returns the negative of the slope of the curve that it is applied to.
  
  
• 00:40:00 This video explains how to create a deep learning model using Pytorch. The model is optimized using gradient descent, and the final coefficients are determined by the mathematical function rectified_linear().
  
  
• 00:45:00 In this video, Jeremy explains how to use gradient descent to optimize parameters in deep learning models. This is the same technique that is used to train the models in real life.
  
  
• 00:50:00 This YouTube video covers practical deep learning for coders and how to go about training models for accuracy and speed. It advises starting with a well-tuned, accurate model and gradually adding more data to see if accuracy improves.
  
  
• 00:55:00 In this video, the author discusses how to calculate the gradient of a function using derivatives. The author recommends multiplying the gradient by a small number, called the learning rate, in order to prevent jumping too far along the gradient and diverging.
  
  
• 01:00:00 In this lesson, the instructor shows how to do matrix multiplication in order to perform deep learning computations on real data. He provides a website that provides an easy way to do this.
  
  
• 01:05:00 The speaker explains how to use deep learning to predict whether passengers on the real Titanic survived or not. They first remove columns that are not relevant to the prediction, and then multiply each row by a coefficient corresponding to the dependent variable. Next, they create a column called "did they embark in Southampton?" and another column called "did they embark in Cherbourg?" and convert these into binary categorical variables. Finally, they take the average of all the coefficients and use it to predict the dependent variable.
  
  
• 01:10:00 This lesson explains how to apply deep learning to coding problems using a linear regression. First, the data is normalized and log-transformed to make it more evenly distributed. Next, coefficients are calculated using a SumProduct function in Excel. Finally, gradient descent is used to optimize the loss function.
  
  
• 01:15:00 In this video, a deep learning model is created from scratch using Microsoft Excel. The model performs better than a regression when predicting survival rates, but is slower and more painful to execute. matrix multiplication is used to speed up the process.
  
  
• 01:20:00 This video provides a brief introduction to deep learning for coders, including a discussion of how matrix multiplication can take a long time to get an intuitive feel for. The next lesson will focus on natural language processing, which is about taking text data and making predictions based on its prose.
  
  
• 01:25:00 This video provides a step-by-step guide on how to use deep learning for classification in non-English languages.
  
  
• 01:30:00 In this video, the presenter discusses the importance of validation sets and metrics in deep learning.

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Lesson 4: Practical Deep Learning for Coders 2022

Lesson 4: Practical Deep Learning for Coders 2022

This video explains how to build a deep learning model for the Coders 2022 competition. The author covers how to create a validation set, how to use competition data to test your model's performance, and how to avoid overfitting in real-world settings.  In this video, Jeremy explains how to use the Pearson correlation coefficient to measure the relationship between two variables, and how to use Pytorch to train a model that behaves like a fast.ai learner. He also discusses a problem with predictions generated by the NLP techniques, and how it can be resolved by using a sigmoid function.

• 00:00:00 This video explains how to fine-tune a pre-trained natural language processing model using a different library than fast.ai.
  
  
• 00:05:00 This video covers the Practical Deep Learning for Coders 2022 algorithm, ULMFiT. ULMFiT was a machine learning algorithm that was first presented in a fast.ai course. ULMFiT was later turned into an academic paper by the author. After training on Wikipedia and IMDB movie reviews, the algorithm was able to predict the sentiment of a review with a 70% accuracy.
  
  
• 00:10:00 In this lesson, Jeremy explained the basics of the Transformers masked language model approach to machine learning. He noted that this approach is more popular than the ULMFiT approach and that there are five layers in the transformer model. John asked a question about how to go from a model that predicts the next word to a model that can be used for classification. Jeremy said that you would need edge detectors and gradient detectors in the first layers and that the last layer would have activations for each category you are predicting. He said that you can train this model by gradually adding a new random matrix to the end.
  
  
• 00:15:00 The Kaggle competition "U.S. Patent Phrase to Phrase Matching Competition" requires a model that can automatically determine which anchor and target pairs are talking about the same thing. In this video, the presenter suggests turning the data into a classification problem to make use of NLP techniques.
  
  
• 00:20:00 This video explains how to use deep learning for classification in a practical way, by working with a data set that is already stored in a comma-separated value (CSV) format. The video also covers how to use pandas to read the data.
  
  
• 00:25:00 This video covers the use of four libraries for deep learning - numpy, matplotlib, pandas, and pytorch. The author recommends reading Wes McKinney's "Python for Data Analysis" if you are not familiar with these libraries. The first step in training a neural network is to tokenize the data, and the second step is to train the network.
  
  
• 00:30:00 In this video, the presenter explains how to tokenize a text into tokens and numericize the tokens to create a Hugging Face "dataset." The presenter recommends using a pre-trained model for tokenization, and describes some of the different models available.
  
  
• 00:35:00 In this video, the presenter explains how to use a tokenizer to tokenize a sentence, and how to turn the tokens into numbers. The data set will be the same as the original data set, but the tokenized data set will be different due to the tokenizer.
  
  
• 00:40:00 The video discusses how to turn strings of text into numbers to allow for deep learning, and explains that it is not necessary to follow a set format, as long as the information is provided. If a field is particularly long, it may be helpful to use a transformer approach.
  
  
• 00:45:00 In this video, the author explains the importance of having separate training, validation, and test sets for machine learning. They show how to plot a polynomial regression and illustrate the difference between underfit and overfit.
  
  
• 00:50:00 The video explains how to create a good validation set for a deep learning model, and how to use competition data to test your model's performance. It also discusses how to avoid overfitting in real-world settings.
  
  
• 00:55:00 In this video, we learn about deep learning and how to build models for Coders 2022 competitions. We learn that a validation set is a set of images that are not used to train the model, and that a test set is another validation set that is used to measure accuracy. We also learn that there are two test sets - the one that is shown on the leaderboard during the competition, and a second test set that is not shown until after the competition is finished.
  
  
• 01:00:00 The "Pearson correlation coefficient" is a widely used measure of how similar two variables are. If your predictions are very similar to the real values, the "Pearson correlation coefficient" will be high.
  
  
• 01:05:00 This 1-minute video explains how to use the Pearson correlation coefficient to measure the relationship between two variables. The correlation coefficient is a measure of how closely two variables are related, and can be used to assess the strength of a relationship between two variables. The correlation coefficient can be visualized using a scatterplot, and can be useful for assessing the data set for which it is being used.
  
  
• 01:10:00 In this video, the presenter discusses how to properly train a deep learning model. They cover topics such as correlation, outliers, and how to properly split data. They then show how to train the model with a “learner” in fast.ai and how to use “batch size” and “epochs” to control how many rows are processed at once.
  
  
• 01:15:00 Hugging Face transformers provides a variety of learning rates for different tasks, and provides a model which is appropriate for classifying sequences from a pre-trained model. It can also identify outliers in the data.
  
  
• 01:20:00 In this lesson, the instructor explains how to use Pytorch to train a model that behaves like a fast.ai learner. He notes that, although outliers should never be deleted, they can be investigated and, if necessary, corrected.
  
  
• 01:25:00 Deep learning is a powerful technology that is being used in multiple application areas. It is relatively beginner-friendly, and the area of natural language processing (NLP) is where the biggest opportunities are. One possible use for deep learning NLP is to create context-appropriate prose for social media, which could have implications for how people see the world.
  
  
• 01:30:00 In this video, John explains how NLP techniques, such as machine learning, can be used to generate text that is biased in favor of a particular viewpoint. The video also discusses a problem with predictions generated by the NLP techniques, and how it can be resolved by using a sigmoid function.

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Lesson 5: Practical Deep Learning for Coders 2022

Lesson 5: Practical Deep Learning for Coders 2022

This video provides a tutorial on how to build and train a linear model using deep learning. The video begins by discussing in-place operations, which change the values of variables within a given function. Next, the video demonstrates how to calculate the loss for a linear model using backward gradient descent. Finally, the video provides a function that initializes and updates coefficients within a linear model. The video concludes by demonstrating how to run the function and print the loss. This video explains how to calculate the best binary split for a given column in a data set. This is particularly useful for machine learning competitions, as it provides a baseline model for comparison.

• 00:00:00 This lesson covers the linear model and neural network from scratch using Jupyter Notebook. The goal is to understand the logic behind the code and to get the expected output.
  
  
• 00:05:00 The video discusses practical deep learning for coders, covering topics such as how to install Kaggle and use its environment variable, how to read CSV files with pandas, and how to impute missing values. It also covers basic concepts in pandas such as the mode and how to use methods and reductions to populate dataframes.
  
  
• 00:10:00 In this lesson, Jeremy covers how to impute missing values in a dataset using the fillna() method in pandas. He explains that in most cases this "dumb" way will be good enough, and that it is important to know the basics of our data set so that common methods are not explained multiple times. Javier asks about the pros and cons of discarding fields that are not used in the model.
  
  
• 00:15:00 In this lesson, the instructor introduces the concept of "dummy variables" and how they can be used to represent categorical data in a more sensible manner. He also shows how to describe all the numeric and non-numeric variables in the data.
  
  
• 00:20:00 In this video, the instructor shows how to turn a column in a dataframe into a tensor, and how to use these Tensors to train a linear model and a neural net. He also shows how to use Pytorch instead of plain Python when doing matrix and element-wise multiplication.
  
  
• 00:25:00 In this video, the instructor discusses how to perform a matrix-vector product in Python. He provides an example of multiplying a matrix by a vector and explains that the result is interesting because mathematicians can perform the same operation using matrix algebra. He also explains that starting a pseudo-random sequence is important in order to produce reproducible results.
  
  
• 00:30:00 In this video, the author explains how broadcasting works and why it is useful. Broadcasting is a technique that allows multiple values to be multiplied together, taking into account the size of each value. This allows for code that is more concise and faster to run on GPU.
  
  
• 00:35:00 This video explains how to optimize deep learning models by calculating the gradient descent loss. The author demonstrates how to do this by creating a function to calculate the loss and then importing the function into a Pytorch session.
  
  
• 00:40:00 This YouTube video provides a tutorial on how to build and train a linear model using deep learning. The video begins by discussing in-place operations, which change the values of variables within a given function. Next, the video demonstrates how to calculate the loss for a linear model using backward gradient descent. Finally, the video provides a function that initializes and updates coefficients within a linear model. The video concludes by demonstrating how to run the function and print the loss.
  
  
• 00:45:00 This 1-minute video discusses how to create an accuracy function for a deep learning model that predicts who survived the Titanic. The function is based on the sigmoid function, which is a mathematical function that asymptotes to one when numbers get too large or too small.
  
  
• 00:50:00 In this video, John explains how to optimize a neural net by using a sigmoid function. He also explains how to handle category block dependent variables using fast.ai.
  
  
• 00:55:00 In this video, the author explains how to build a model from scratch in Python using Pytorch, and how to submit the model to Kaggle. The author mentions that the operator "matrix-multiply" means matrix-multiply, but that Python does not come with an implementation of the operator.
  
  
• 01:00:00 In this video, the presenter explains how to create a neural network using Pytorch. The first step is to create a matrix with sums of the coefficients of the first column. This matrix is then multiplied by the training data vector. The second step is to create a second matrix with the hidden activations. This matrix is then multiplied by the coefficients of the first column. The final step is to centralize the vectors and train the neural network.
  
  
• 01:05:00 In this video, Jeremy explains how to train a neural network using deep learning. He explains that, in order to train the network, he first needs to calculate the gradients and initialize the coefficients. Next, he executes update_coeffs(), which subtracts the coefficients from the gradients. Finally, he trains the network and compares the results to a linear model.
  
  
• 01:10:00 This video explains how to initialize deep learning models, how to update the coefficients, and how to loop through all the layers. It also discusses why deep learning may not be as effective on small data sets and how to get good results with deep learning models.
  
  
• 01:15:00 In this lesson, the author teaches how to use a deep learning framework, and shows how it is much easier than doing it from scratch. The author also provides a tutorial on feature engineering with Pandas.
  
  
• 01:20:00 In this video, trainer demonstrates how to use the fastai library to recommend a learning rate for a deep learning model. He shows how to run multiple epochs and compares the predictions of the model with the predictions of two other models. Finally, he demonstrates how to use the ensemble function to create a set of five predictions that is averaged over the rows.
  
  
• 01:25:00 In this video, John explains how random forests work and why they're a popular machine learning algorithm. He also shows how to use a handy shortcut to import all the necessary modules.
  
  
• 01:30:00 In this video, Pandas explains how deep learning works and how to apply it to coding problems. The random forest algorithm is introduced, and it is shown that this technique can be used to improve the accuracy of predictions made using a categorical variable.
  
  
• 01:35:00 This 1-paragraph summary explains how to score a binary split using a simple method of adding standard deviation scores of the two groups of data. The best split point is found by calculating the smallest index into the list of split points with the smallest score.
  
  
• 01:40:00 In this lesson, the instructor explains how to calculate the best binary split for a given column in a data set. This is particularly useful for machine learning competitions, as it provides a baseline model for comparison.

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Lesson 6: Practical Deep Learning for Coders 2022

Lesson 6: Practical Deep Learning for Coders 2022

This YouTube video provides a guide on how to get started with deep learning for coders. The main focus is on practical deep learning for coders, with tips on how to set up a competition, get a validation set, and iterate quickly. The video also discusses the importance of feature importance and partial dependence plots, and how to create them using a machine learning model.

This video provides an overview of how to use deep learning to improve the accuracy of coding projects. It explains that data sets can often have a wide variety of input sizes and aspect ratios, which makes it difficult to create accurate representations with rectangles. It suggests using square representations instead, which have been found to work well in most cases.

• 00:00:00 In this lesson, the authors show how to create a decision tree to predict which males survive the Titanic.
  
  
• 00:05:00 The video discusses how to create a decision tree classifier with at most four leaf nodes. The tree can be automatically generated and the code to calculate the Gini coefficient is provided. The decision tree's mean absolute error is calculated to be 0.407.
  
  
• 00:10:00 This video explains how to build a decision tree to predict passenger fare classifications using data from a Kaggle competition. Decision trees are efficient and don't require preprocessing, making them a good option for tabular data.
  
  
• 00:15:00 Leo Breiman's "bagging" technique is used to create a large number of unbiased models that are better than any individual model. This is done by randomly selecting a subset of the data each time a decision tree is built and using that data to train the model.
  
  
• 00:20:00 In this lesson, we learned how to create a Random Forest, a machine learning algorithm that is simple to implement and that performs well on small data sets. We also showed how to use feature importance plots to help us determine which features are most important in the training data.
  
  
• 00:25:00 In this video, John covers the basics of deep learning, including how Random Forests work and why increasing the number of trees always leads to a better error rate. Jeremy then goes on to explain how Random Forests can be used to predict outcomes for large data sets without the need for a validation set.
  
  
• 00:30:00 The video explains how to calculate the Out-of-Bag Error or OOB error, which is a measure of the accuracy of predictions made on data not used in the training of a machine learning model. It notes that if the OOB error is high, this suggests that the model is not correctly predicting the data.
  
  
• 00:35:00 The video discusses the importance of feature importance and partial dependence plots, and how to create them using a machine learning model.
  
  
• 00:40:00 In this video, Jeremy explains how Random Forest models work and how to interpret their feature importance plots. He also mentions that Random Forest models are more reliable than other explainability techniques.
  
  
• 00:45:00 Random Forests are a machine-learning algorithm that is particularly good at making predictions. However, adding more trees will make the model more accurate, and overfitting is not an issue. Gradient Boosting is a machine-learning algorithm that is similar to Random Forests, but instead of fitting a tree over and over again, it fits very small trees that result in little data splitting.
  
  
• 00:50:00 The video explains how a gradient boosting machine (GBM) is more accurate than a random forest, but that you can overfit with a GBM. The walkthrough demonstrates how to pick a Kaggle competition and achieve the top spot.
  
  
• 00:55:00 This YouTube video provides a guide on how to get started with deep learning for coders. The main focus is on practical deep learning for coders, with tips on how to set up a competition, get a validation set, and iterate quickly.
  
  
• 01:00:00 This video explains how to use FastKaggle to train deep learning models. It explains that you need to be careful when working with images as the size can change depending on the aspect ratio. The video also shows how to resize the images using a function called "squish."
  
  
• 01:05:00 In this video, the instructor discusses how to use fast AI library show_batch() to quickly see what data looks like for machine learning models. He recommends using resnet26d for fast training performance and accuracy.
  
  
• 01:10:00 The video demonstrates how to submit a deep learning model to Kaggle in less than a minute by using a dataloader and a CSV file that includes the model's predictions and labels.
  
  
• 01:15:00 The presenter shares his strategy for creating public notebooks on Kaggle, which involves duplicating and renaming notebooks as needed in order to keep them organized. He notes that this low-tech approach works well for him and that he typically only submits one notebook at a time.
  
  
• 01:20:00 The presenter provides a brief overview of different methods for deep learning, including AutoML frameworks and random forests. He recommends using a learning rate finder to avoid overtraining models and recommends using GPUs for deep learning if possible.
  
  
• 01:25:00 In this lesson, the author explains how to speed up an iteration on a Kaggle competition using a different convolutional neural network (CNN) architecture. He also shows how to use a rule of thumb to choose the right CNN size.
  
  
• 01:30:00 In this video, the presenter discusses how to improve a deep learning model's performance by using different pre-processing techniques, including cropping and padding. He also notes that Test Time Augmentation (TTA) can improve the performance of a model by averaging multiple versions of an image.
  
  
• 01:35:00 In this video, Jeremy discusses how to improve the accuracy of a computer vision model using deep learning. He notes that the model's accuracy can be improved by varying the images it is trained on, and provides an example of how to do this using pandas. He also explains how fast deep learning algorithms work and how to use TTA, or test time augmentation, to speed up the training process. Finally, he provides a summary of the questions Victor and John asked.
  
  
• 01:40:00 In this video, Jeremy explains how deep learning can be used to improve the accuracy of coding projects. He notes that data sets can often have a wide variety of input sizes and aspect ratios, which makes it difficult to create accurate representations with rectangles. He suggests using square representations instead, which have been found to work well in most cases.

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Lesson 7: Practical Deep Learning for Coders 2022

Lesson 7: Practical Deep Learning for Coders 2022

In Lesson 7 of Practical Deep Learning for Coders 2022, Jeremy explains how to scale up deep learning models by reducing the memory needed for larger models. He demonstrates a trick called gradient accumulation, which involves not updating the weights every loop of every mini-batch, but doing so every few times instead, allowing for larger batch sizes to be used without needing larger GPUs. Additionally, Jeremy discusses k-fold cross-validation and creating a deep learning model that predicts both the type of rice and the disease present in the image using a different loss function called cross-entropy loss. Overall, the video provides practical tips and tricks for building more complex deep learning models.

In this video, the speaker explores the creation of recommendation systems using collaborative filtering and dot product in PyTorch. He describes matrix multiplication prediction of movie ratings and calculates the loss function, a measure of how well the predicted ratings match the actual ratings. He introduces the concept of embeddings, which allows for a speedup in matrix multipliers with dummy variables. The speaker then explains how to add bias and regularization to the matrix to differentiate user ratings and prevent overfitting. Finally, the topic of hyperparameter search is discussed, emphasizing the need for granular data for accurate recommendations. Overall, the video breaks down complex deep learning concepts to create a practical understanding for viewers.

• 00:00:00 In this section, the instructor introduces a simple trick for scaling up models further, which involves reducing the memory needed for larger models. When larger models are used, more parameters mean they can find more intricate features, thereby making them more accurate. However, larger models have a downside because their activations or gradients that need to be computed consume a lot of GPU memory, and if the available memory is insufficient, it results in an error message. The instructor explains how to circumvent this issue and use an x-large model even on Kaggle's 16 Gig GPU.
  
  
• 00:05:00 In this section of the video, Jeremy discusses the practicalities of running deep learning models on Kaggle and how to use quick hacky methods to determine memory usage of models. He demonstrates a trick called gradient accumulation which can be used if the model crashes with a "cuda out of memory error" to avoid the need to purchase a larger GPU. By adjusting the batch size and number of images, one can ensure the model uses the minimum amount of memory possible without affecting the learning rates.
  
  
• 00:10:00 In this section, the speaker discusses the concept of gradient accumulation, which is the idea of not updating the weights every loop through for every mini-batch, but doing so every few times instead. This allows for larger batch sizes to be used without needing larger GPUs, as the gradient can be accumulated over multiple smaller batches. The results are numerically identical for architectures that do not use batch normalization, but may introduce more volatility for those that do. Overall, gradient accumulation is a simple idea with significant implications for training larger models.
  
  
• 00:15:00 In this section, Jeremy discusses questions from the forum relating to lr_find() and gradient accumulation. He explains that lr_find() uses the data loaders batch size and that gradient accumulation allows for experimenting with different batches to find the optimal size for different architectures. Jeremy recommends picking the largest batch size that can fit in your GPU, but mentions that it's not always necessary to use the largest batch size. The rule of thumb is to divide the batch size by two and divide the learning rate by two. Finally, Jeremy demonstrates how to use gradient accumulation in fastai by dividing the batch size by the desired accumulation value and passing in the GradientAccumulation callback when creating the learner, allowing him to train multiple models on a 16GB card.
  
  
• 00:20:00 In this section, the presenter discusses using pre-trained transformer models vit, swinv2, and swin, which have fixed sizes. To work around this, the final size must be square and of the required size. The presenter uses a dictionary of architectures and pre-processing details, switching the training path back to using all the images and looping through each architecture and transform size to train each model. The training script returns tta predictions, which are appended to a list that is later averaged out via bagging to create a list of indexes for each disease. By submitting entries regularly, the presenter improved their results and was able to secure a top leaderboard position.
  
  
• 00:25:00 In this section, Jeremy discusses the concept of k-fold cross-validation and how it is similar to what he has done with ensembling models. He explains that k-fold cross-validation is where the data is split into five subsets, and models are trained on each subset with non-overlapping validation sets, which are then ensembled. While it could potentially be better than his method, Jeremy prefers ensembling because it allows the easy addition and removal of models. Jeremy also discusses gradient accumulation and how there are no real drawbacks or potential gotchas, and recommends buying cheaper graphics cards with less memory rather than expensive ones. Lastly, he mentions that Nvidia is the only game in town for GPUs, and the consumer RTX cards are just as good as the expensive enterprise cards.
  
  
• 00:30:00 In this section, Jeremy discusses the benefits of investing in a GPU for deep learning and acknowledges that they can be expensive due to their use in cloud computing. He also touches upon how to train a smaller model to produce the same activations as a larger one, which will be covered in Part 2. The rest of the video focuses on building a model that predicts both the disease and type of rice of an image, which requires a data loader with two dependent variables. Jeremy explains how to use the DataBlock to create a loader with multiple dependent variables, and demonstrates how to differentiate between input and output categories.
  
  
• 00:35:00 In this section, the instructor explains how to create a deep learning model that predicts both the type of rice and the disease present in the image. To achieve this, the get_y function must take an array with two different labels. One is the name of the parent directory, as it indicates the disease, and the second is the variety. The teacher creates a function that takes the location in the data frame of the file name and returns the variety column. Finally, they create a model that predicts 20 things: the probability of each of the 10 diseases and each of the 10 varieties. The error rate metric must be modified to handle three things instead of two to work with the new dataset.
  
  
• 00:40:00 In this section, the speaker explains the need for a different loss function called cross entropy loss when the dependent variable is a category. While fastai's vision_learner guessed and used cross-entropy loss earlier, the speaker now explains how it works in detail with the help of a spreadsheet. Starting with the output of a model featuring five categories, the speaker demonstrates how to convert model outputs into probabilities using the softmax function. Once the outputs are probabilities, the cross-entropy loss function is used to measure the difference between the predicted probabilities and the actual probabilities, and determine how well the model is performing.
  
  
• 00:45:00 In this section, we learn about softmax and how it is used to predict one specific thing from categories that are chosen ahead of time. The formula used to calculate cross-entropy loss involves multiplication of probabilities and actual target values, and then taking the sum. The log of softmax is utilized to speed up calculations. The result is a single probability value which is then summed up for each row in order to calculate the cross-entropy loss.
  
  
• 00:50:00 In this section, the instructor explains the binary cross-entropy loss function and how to use it with multiple targets. He notes that pytorch has two versions of loss functions, a class and a function, and shows how to use them. When creating a multi-target model, the vision learner requires twenty outputs, with ten predicting the disease and ten predicting the variety. The instructor demonstrates how to create this model, and then trains it. Overall, this model is identical to the previous model except for the addition of the second set of targets.
  
  
• 00:55:00 In this section, we learn about how a model knows what it's predicting through its loss function. The first ten columns of input values predict the disease probability, and the second ten represent the probability of variety. Using cross-entropy, we factor in both the disease and variety target values to create a loss function based on predicting these two values. The loss function reduces when the first ten columns make good predictions of diseases and the second ten for variety, making the coefficients better at using each column effectively. We calculate and track error rates for both disease and variety predictions throughout the training epoch. Training for longer using a multi-target model can sometimes result in better disease prediction than a single target model due to certain features helping recognize different targets.
  
  
• 01:00:00 In this section of the video, the speaker discusses the benefits of building models that predict multiple things and encourages viewers to experiment with models on small datasets. He also introduces the Collaborative Filtering Deep Dive notebook, which uses a dataset of movie ratings to teach the concept of recommendation systems. He explains that this type of data is common in industries such as recommendation systems and provides an example of a cross-tabulation to better understand the dataset. The speaker then takes a break before diving into the next notebook.
  
  
• 01:05:00 In this section, the speaker explains how to fill in gaps in a collaborative filtering dataset. Collaborative filtering helps recommend a product to a user by using data collected from many users. To figure out whether a user might like a particular movie, the speaker proposes a method of multiplying corresponding values of user preferences and the type of movie through vector analysis. However, since we are not provided with any information about users or movies, the speaker suggests creating latent factors to fill in for the missing data points. Using supposed latent factors, the speaker proposes using SGD to find the correlation and generate hypothesis.
  
  
• 01:10:00 In this section, the video describes how to use matrix multiplication to predict movie ratings for users based on their historical ratings. The tutorial assigns random values for latent factors for movies and users and performs the dot product to predict the ratings. A loss function is then calculated, and optimization is performed using the Data Solver tool. The video demonstrates that the predicted ratings improved by comparing them with actual ratings after optimization. The matrix completion technique and collaborative filtering, where users with similar tastes are recommended similar movies, are also introduced.
  
  
• 01:15:00 In this section, the video discusses using collaborative filtering and dot products in PyTorch. The cosine of the angle between two vectors can approximate correlation, and they are the same once normalized. Excel is used to explain the necessary calculations in PyTorch. The video also notes that embeddings, often thought of as a complex mathematical tool, are actually arrays that are used to look things up in. The video tries to break down confusing jargon to make deep learning easier to understand for everyone.
  
  
• 01:20:00 In this section, Jeremy explains how to use collaborative filtering data loaders in PyTorch to work with movie ratings data. He merges the movie table with the ratings table to get the user id and name of the movie. The CollabDataLoaders function is used to load data from the data frame with ratings, user ID, and item title columns. Then, he creates user and movie factors using a matrix of random numbers, where their number of columns is equal to the number of factors he wants to create. He also mentions that he uses a predetermined formula for determining the number of factors, which is derived from his intuition and tested through fitting a function.
  
  
• 01:25:00 In this section, the speaker explains that a one-hot-encoded vector can be used to look up an index number in the vector, which is the same as taking the dot product of a one-hot-encoded vector with something. Embeddings are then introduced as a computational shortcut for multiplying something by a one-hot-encoded vector, allowing for a speedup in doing matrix multipliers with dummy variables. The speaker also introduces the creation of a Pytorch model, which is a class that includes a superclass called Module that gives some additional functionality. The dot product object is used as an example of how to create a model, which calls the dunder init method and creates embeddings of users by factors and movies by vectors.
  
  
• 01:30:00 In this section, the instructor explains how to use PyTorch to call a method called "forward" to calculate the model. The object itself and the data being calculated on are passed into "forward". Using the dot product and passing the data through PyTorch is much faster than using Excel. However, the model does not work well since, as an example, it predicts values larger than the highest possible value while nobody rated a movie under a one. The instructor remedies this by using the sigmoid function to squash the prediction into a range between zero and 5.5. Despite this change, the loss does not improve considerably, but the instructor introduces a new observation of some users having high ratings, suggesting involving user bias could improve the model.
  
  
• 01:35:00 In this section, the speaker demonstrates how to add bias to the matrix used in the movie recommendation model. By adding these biases, it is possible to differentiate users who tend to give lower or higher ratings. The speaker also discusses how to avoid overfitting by using weight decay or L2 regularization. The speaker explains that this can be achieved by adding the sum of the weights squared to the loss function. Overall, the section provides a useful introduction to the topic of biases and regularization in deep learning models.
  
  
• 01:40:00 In this section, the video discusses the use of weight decay as a form of regularization to prevent overfitting in deep learning models. By finding the right mix of weights that are not too high, but high enough to be useful at predicting, the model can get the lowest possible value of the loss function. The weight decay coefficient can be passed into the fit method and the defaults are normally fine for vision applications, but for tabular and collaborative filtering, users should try a few multiples of 10 to see what gives the best result. Regularization is about making the model no more complex than it has to be, with the higher values of weight decay reducing overfitting but also reducing the capacity of the model to make good predictions.
  
  
• 01:45:00 In this section, Jeremy and John discuss the topic of hyperparameter search and how it is often used in building individual models. However, there are no rules other than Jeremy's rule of thumb when it comes to performing an exploration for hyperparameters. In response to a question about whether recommendation systems can be built based on the average ratings of users' experience instead of collaborative filtering, Jeremy explains that it would not be ideal if all one has is purchasing history. Instead, granular data such as demographic information about users and metadata about products are needed to make accurate recommendations.

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Lesson 8 - Practical Deep Learning for Coders 2022

Lesson 8 - Practical Deep Learning for Coders 2022

This video covers the basics of deep learning for coders. It explains how to create parameters for deep learning models using the Pytorch library, how to use PCA to reduce the number of factors in a data set, and how to use a Neural Net to predict the auction sale price of industrial heavy equipment.

This YouTube video provides an overview of deep learning for programmers. The speaker explains that tenacity is important in this field, and advises that if you want to be successful, you should keep going until something is finished. He also recommends helping other beginners on forums.fast.ai.

• 00:00:00 In this lesson, Pytorch takes care of creating and managing parameters for a neural network, automatically making sure that the coefficients and weights are initialized in the correct way. This saves the user from having to remember this information or write code to do it.
  
  
• 00:05:00 This video explains how to create parameters for deep learning models using the Pytorch library. Pytorch will automatically initialize these parameters based on a randomly generated distribution.
  
  
• 00:10:00 This video shows how to build a Pytorch embedding layer from scratch, using the same code and concepts as the original. The video then demonstrates how the layer predicts movie preferences by looking at past movie preferences.
  
  
• 00:15:00 The video demonstrates how to use the fast.ai collaborative learner application to predict which movie a user might like. The application uses latent factors ( movies and factors ) to calculate a user's bias, which is then used to predict which movie the user might like.
  
  
• 00:20:00 This video explains how to use PCA to reduce the number of factors in a data set. It also covers the Bootstrapping Problem, which is the question of how to recommend new products to customers when you have no previous history with them.
  
  
• 00:25:00 This video covers the basics of deep learning for coders, including a sequential model, how to use Pytorch's functionality to create a model easily, and how to fit the model in a collaborative learner. Jona asks the presenter a question about the issue of bias in collaborative filtering systems, and the presenter provides a general answer about the problem.
  
  
• 00:30:00 In this video, Jeremy explains how embeddings work in Collaborative Filtering and NLP, and how they can be used to interpret Neural Networks.
  
  
• 00:35:00 In this video, the author demonstrates how to use a Neural Net to predict the auction sale price of industrial heavy equipment, using a Random Forest and a Tabular Learner. The author notes that the Neural Net created using the Tabular Learner is almost identical to the Neural Net created manually.
  
  
• 00:40:00 Neural networks can be thought of as a type of machine learning algorithm that takes in data as input, and uses that data to create predictions or outputs. Neural networks are composed of layers of nodes (called neurons), which are interconnected to create a graph. The inputs to a neural network can be categorical (e.g. a category such as cars or flowers), or continuous (i.e. a number). Neural networks can be used to predict the outcomes of different outcomes (e.g. sales at different stores), or to guess the contents of a new input (e.g. the geographical location of a given set of data points).
  
  
• 00:45:00 In this video, we learn about convolutions, which are a type of matrix multiplication used in convolutional neural networks. We see an example of this in action, and then discuss how to create a top edge detector using convolutional neural networks.
  
  
• 00:50:00 The video explains how to perform a convolution, a mathematical operation that takes two arrays of data and combines their elements, producing a result that is usually higher on the left side of the array than on the right side. A convolution is performed on a 3 by 3 kernel, which is repeated multiple times to produce a deep learning layer.
  
  
• 00:55:00 The video explains how deep learning works by showing how two filters, one for horizontal edges and one for vertical edges, are combined to create a single activation for recognizing numbers. The older way of doing this, which used max pooling, resulted in fewer activations and eventually one left. The newer way, which uses a technique called "Max Pooling with sliding windows," keeps going until all activations are used and produces a more accurate result.
  
  
• 01:00:00 In this video, the presenter explains how deep learning is done in the 21st century. Today, deep learning is done differently than it was 10 years ago, and the presenter provides an example of how this change works. Instead of Max Pooling, deep learning algorithms now use Stride 2 Convolution. Additionally, deep learning models now use a single dense layer at the end instead of a Max Pool layer. Finally, the presenter provides a brief overview of how fast.ai handles deep learning training and prediction.
  
  
• 01:05:00 In this YouTube video, the author shows how convolution is the same as matrix multiplication, and how to calculate a convolution using the two methods. He also discusses Dropout, a technique for reducing the impact of random noise in neural networks.
  
  
• 01:10:00 In this lesson, the author describes how Dropout Layers help to avoid overfitting in neural networks. The more dropout you use, the less good it will be on the training data, but the better it ought to generalize. This comes from a paper by Geoffrey Hinton's group, which was rejected from the main neural networks conference, then called NIPS, now called NeurIPS.
  
  
• 01:15:00 This video covers the basics of deep learning, including the types of neural networks and how to implement them. It also covers how to use various training methods and how to evaluate the performance of a neural network. Finally, the video provides advice on how to continue learning deep learning.
  
  
• 01:20:00 Lucas asks how to stay motivated in deep learning, and he notes that the field is quickly becoming skewed towards more expensive and large-scale models. He wonders if he would still be able to train reasonable models with a single GPU in the future. Overall, this video provides an overview of how to stay motivated in deep learning and how to stay up-to-date with the latest research.
  
  
• 01:25:00 This YouTube video provides a brief overview of deep learning and its practical application to coding. The video discusses how the deep learning success of DawnBench was due to the team's use of common sense and smarts, and how anyone can apply deep learning to their own problem domains. The video also touches on the importance of formal education in the field of machine learning, and how live-coding sessions help reinforce learning.
  
  
• 01:30:00 Jeremy shared some productivity hacks, including spending time learning something new every day and not working too hard.
  
  
• 01:35:00 This YouTube video is a lesson on deep learning for programmers, and the speaker explains that tenacity is important in this field. He advises that if you want to be successful, you should keep going until something is finished, even if it is not the best quality. He also recommends helping other beginners on forums.fast.ai.

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Lesson 9: Deep Learning Foundations to Stable Diffusion, 2022

Lesson 9: Deep Learning Foundations to Stable Diffusion, 2022

This video provides an introduction to deep learning, discussing how stable diffusion models work and how they can be applied to generate new images. The video includes a demonstration of how to use the Diffusers library to create images that look like handwritten digits. It also introduces the concept of stable diffusion, which is a method for training Neural Networks. The basic idea is to modify the inputs to a Neural Network in order to change the output. In this video, the instructor discusses how to create a Neural Net that will be able to correctly identify handwritten digits from noisy input. This video discusses how to train a machine learning model using a deep learning algorithm. The model is initialized with a set of latent variables (representing the data) and uses a decoder to understand the raw data. Next, a text encoder is used to create machine-readable captions for the data. Finally, a U-Net is trained using the captions as input, and the gradients (the "score function") are used to adjust the noise levels in the training data.

• 00:00:00 In this lesson explains how deep learning works and how to apply it to real-world problems. However, because the concepts and techniques described in this lesson are likely to become outdated in the near future, the majority of the video is spent teaching how to use stable diffusion, a deep learning algorithm that will still be applicable in 2022.
  
  
• 00:05:00 The course is moving quickly and new papers have shown that the number of steps required to generate a stable diffusion model has gone down from a thousand to four or fifty-six. The course will focus on the foundations of the model and how it works.
  
  
• 00:10:00 The course provides deep learning foundations, discussing how Stable Diffusion models work, and providing resources for more in-depth learning. In 2022, GPUs for deep learning will become more expensive, so it is important to take note of current recommendations.
  
  
• 00:15:00 This YouTube video provides a short introduction to deep learning, outlining the foundations of stable diffusion. The author provides a set of Colab notebooks, "diffusion-nbs", that can be used to explore the basics of deep learning. The video concludes with a recommendation to play around with the provided material and explore other resources.
  
  
• 00:20:00 In this lesson, the basics of Deep Learning are covered, including how to create a stable diffusion algorithm. Afterwards, the Diffusers library is introduced, and how to save a pipeline for others to use.
  
  
• 00:25:00 This lesson discusses the foundations of deep learning and how to use Colab to create high-quality images. The 51 steps it takes to create an image are compared to the three to four steps available as of October 2022.
  
  
• 00:30:00 In this lesson, the instructor demonstrates how to create images using deep learning. He demonstrates how to use "guidance scale" to control how abstract the images are.
  
  
• 00:35:00 This video explains how to use a deep learning model to generate images that look like the original drawing, using a technique called stable diffusion.
  
  
• 00:40:00 In this lesson, the instructor explains how to train machine learning models with the stable diffusion algorithm. They explain that the algorithm is useful for generating images that are similar to the examples that have been provided. The instructor also shares an example of how the stable diffusion algorithm was used to generate an image of a teddy that is similar to the original teddy.
  
  
• 00:45:00 In this video, the instructor introduces the concept of stable diffusion, which is a mathematical approach that is equivalent to the traditional approach but is more conceptually simple. He explains that by using a function that can determine the probability that an image is a handwritten digit, you can generate new images that look like handwritten digits.
  
  
• 00:50:00 In this video, an instructor explains how to calculate the gradient of the probability that an inputted image is a handwritten digit, using deep learning.
  
  
• 01:05:00 This video introduces the idea of stable diffusion, which is a method for training Neural Networks. The basic idea is to modify the inputs to a Neural Network in order to change the output.
  
  
• 01:10:00 In this video, the instructor discusses how to create a Neural Net that will be able to correctly identify handwritten digits from noisy input. They first discuss how to create a training dataset and then go on to explain how to train the Neural Net.
  
  
• 01:15:00 This video introduces the concept of deep learning and stable diffusion, which is a way to predict the noise in a digit image. The Neural Net predicts the noise and the loss function is simple: taking the input and predicting the noise.
  
  
• 01:20:00 The Neural Network in this video is trying to predict the noise that was added to the inputs. It does this by subtracting the bits that it thinks are noise from the input. After doing this multiple times, it eventually gets something that looks more like a digit.
  
  
• 01:25:00 In this video, the instructor shows how a Neural Net, called the U-Net, can be used to approximate an image. The problem is that the U-Net requires a lot of storage, which can be a problem for Google with its large cloud of TPUs.
  
  
• 01:30:00 The video explains how to compress an image using deep learning. First, an image is compressed by putting it through a layer of stride two convolutional layers. This process is repeated until the image is reduced to a 64x64x4 version. Next, the image is saved as a Neural Network layer. Finally, the Neural Network is used to compress images of different sizes.
  
  
• 01:35:00 The video discusses how a loss function can be used to teach a Neural Net how to compress an image, resulting in a smaller file. The compression algorithm works well and can be used to share images between two people.
  
  
• 01:40:00 This video provides a tutorial on how to train a deep learning model using latent data. Latents are a special type of data that are not directly observed and are used to train a deep learning model. Latents are created by encoding a picture's pixels using a neural network. The encoding process creates a latent representation of the picture. The decoder uses this latent representation to generate the original picture.
  
  
• 01:45:00 This video explains how a Neural Network can learn to predict noise better by taking advantage of the fact that it knows what the original image was. This is useful because, when fed the number 3, for instance, the model will say that the noise is everything that doesn't represent the number 3.
  
  
• 01:50:00 The video explains how two neural networks can be used to encode text and images. The first neural network is used to encode text, and the second neural network is used to encode images. The goal is for the two networks to produce similar outputs for a given input. The similarity of the outputs is determined by the dot product of the features of the input and the features of the output.
  
  
• 01:55:00 This video explains how to create a CLIP text encoder, which is a type of machine learning model that can produce similar embeddings for similar text inputs. This is important because it allows for multimodal text recognition and synthesis.
  
  
• 02:00:00 In this video, the instructor explains how to train a machine learning model using a deep learning algorithm. The model is initialized with a set of latent variables (representing the data) and uses a decoder to understand the raw data. Next, a text encoder is used to create machine-readable captions for the data. Finally, a U-Net is trained using the captions as input, and the gradients (the "score function") are used to adjust the noise levels in the training data.
  
  
• 02:05:00 In this video, the author describes how deep learning algorithms work, and how they try to find the best guess for a latent (unknown) image. The author also describes how to tweak the algorithm's parameters to improve results.
  
  
• 02:10:00 The video discusses how differential equation solvers, such as optimizers, use similar ideas to deep learning models. The video discusses how Perceptual Loss and other loss functions can be used to improve the accuracy of deep learning models. The video provides a sneak peak of the next lesson, in which the code for a deep learning pipeline will be explained.
  
  
• 02:15:00 This video discusses some new research directions in deep learning that are likely to improve stability and diffusion of the technology.

[MetaQuotes]

Challenges in Deep Learning (Dr Razvan Pascanu - DeepMind)

Challenges in Deep Learning (Dr Razvan Pascanu - DeepMind)

Dr. Razvan Pascanu from DeepMind discusses several challenges in deep learning in this video. He highlights the importance of adaptability and shifting focus from performance metrics, and suggests that the limitations of computational resources in deep learning systems can actually be beneficial. Moreover, he explores the challenges in continual learning and the subfield of machine learning related to this, including the impact of size and architecture on the performance of deep learning models. Dr. Pascanu also discusses the role of stochastic gradient descent, the importance of explicit biases, and the concept of pre-training and adding inductive biases in deep learning models.

Dr. Razvan Pascanu of DeepMind discusses the issue of forgetting in deep learning and how models can recover from it. While some knowledge may still remain after forgetting occurs, it's difficult to determine how much information is lost. Dr. Pascanu mentions how recent papers on targeted forgetting have been focusing on data privacy, but more research and focus is needed in this area.

• 00:00:00 In this section, Dr. Razvan Pascanu discusses his insights on the importance of adaptation in deep learning. Despite the emphasis on performance metrics, Pascanu highlights the limiting effects of continuously optimizing for this singular measure. The lack of adaptability in fixed systems when faced with unpredictable changes in the environment, the focus on one-size-fits-all solutions, the lack of well-defined performance metrics in more complex scenarios, and the scaling up of systems to solely overcome bottlenecks such as limited data and compute resources are some of the issues prompting the need for a shift in focus towards adaptation in deep learning.
  
  
• 00:05:00 In this section, Dr. Razvan Pascanu discusses the limitations of focusing solely on performance in deep learning, and suggests exploring systems that can continuously adapt to change. He highlights issues of coverage and data representativeness when learning on large datasets, as well as the challenge of evaluating a system's out-of-distribution generalization capacity. Pascanu suggests that thinking about adaptability changes the perspective and can help address some of these issues. He cites Thomas Griffiths' argument that the reason we cannot understand certain moves made by AlphaGo is because we tend to decompose problems into sub-goals, while the agent only cares about the final performance. Pascanu concludes that switching perspectives may not solve all problems, but could lead to new insights and approaches.
  
  
• 00:10:00 In this section, Dr. Pascanu discusses the idea that limitations can be beneficial in deep learning systems. While humans have cognitive limitations that shape the way we think, machine learning systems have computational limitations that shape how they learn. If we think of limitations as a hurdle that needs to be overcome, we might miss the benefits of these limitations. Limiting computational budgets can force the system to find combinatorial solutions, which can lead to better results. Dr. Pascanu believes that instead of focusing solely on performance, we should also take into account the cost of learning, the cost of inference, and the amount of hard-coded information. However, there are still challenges to overcome in terms of the relationship between data distribution and data points, and Dr. Pascanu highlights the importance of exploring different tools such as category theory to tackle these challenges.
  
  
• 00:15:00 In this section, Dr. Razvan Pascanu-DeepMind discusses why continual learning is important and the desiderata of a system that can continuously learn efficiently. He highlights that there are many flavors of continual learning, making it difficult to define and pinpoint each specific issue. Moreover, the definition of continual learning can also create contradictions, and some of the problems become more visible depending on the tasks and benchmarks that are used. Dr. Pascanu-DeepMind suggests that one way to ground continual learning is to focus on real problems like continuous semi-supervised learning, which has a practical application, making it easier to see if progress is being made. Another way is to focus on reinforcement learning.
  
  
• 00:20:00 In this section, Dr. Razvan Pascanu discusses the challenges of continued learning in reinforcement learning (RL) and the techniques that have been developed so far to mitigate this issue. Since data in RL is non-stationary, it’s necessary to deal with continued learning in order to make function approximations work. Several techniques have been developed based on Brute Force methods such as replay buffers to prevent catastrophic forgetting, league of experts in Starcraft, and sales play in AlphaGo. However, these methods can become expensive, and there is always a need to reduce these costs. Pascanu presents an interesting paper that argues that RL and supervised learning differ in that transferring features doesn't help as much in RL systems, and the focus should be on controlling the quality of the data and centralizing the actor and critic.
  
  
• 00:25:00 In this section, the speaker discusses the challenges in continuing learning, pointing out that there are aspects of continual aligning that Supervised learning cannot emulate. The speaker mentions a paper that lists these differences between RL and Supervised learning. Additionally, the speaker discusses how continuing learning can be thought of as a tracking problem rather than convergence to a point. The speaker mentions the loss of plasticity that occurs when fine-tuning on new data and how this can impact generalization. Finally, the speaker discusses credit assignment in neural networks and how the gradient is computed independently for each weight, which can cause conflicting votes that affect the average.
  
  
• 00:30:00 In this section of the video, Dr. Razvan Pascanu talks about learning on the fly, which is similar to a tug-of-war game where every example exerts a force on the weight, and learning occurs when equilibrium is reached between these forces. ID data is essential in this process as it ensures that all forces are present. Additionally, learning indirectly teaches knowledge, with various related concepts learned simultaneously. It is suggested that the learning Dynamics, optimizer, and gaming automation middleware can be improved to create a more efficient learning process to obtain more knowledge from less data.
  
  
• 00:35:00 In this section, Dr. Razvan Pascanu of DeepMind discusses the challenges in the subfield of machine learning known as continual learning, which involves trying to teach a system new things without it forgetting what it previously learned. The field is under-specified, benchmarks are not well-defined, and there are disagreements on what people care about. One issue is the quality of data and the trade-offs between learning and forgetting, which is highly dependent on how the benchmark is defined. The goal is to come up with benchmarks that are more natural, but even the definition of "natural" is not agreed upon.
  
  
• 00:40:00 In this section, Dr. Razvan Pascanu discusses the concept of AGI systems and their relationship with human intelligence. He explains that although building an AGI system that resembles humans might be desirable for ease of interpretation, it is not necessary. The sub-goals used in AGI learning are efficient and aid in compositional generalization, allowing for quicker learning of new things. Pascanu also discusses how the implicit biases of deep learning models can lead to errors and how explicit biases can be used to improve models. He gives an example of how continual learning can be improved with the use of over-parameterized systems in the area of very low curvature.
  
  
• 00:45:00 In this section of the video, Dr. Razvan Pascanu discusses the impact of the size and architecture of a model on its performance in deep learning. He notes that scale alone has a significant impact on how well a system forgets, and choosing the right architecture makes a huge difference. The field typically ignores the impact of architecture choices and often compares architectures unfairly. Dr. Pascanu also highlights the role of optimization in deep learning and suggests that over-parametrization results in many solutions with zero training error. As the number of solutions explodes, the closest solution to initialization converges, and the system still depends on initialization conditions. He mentions examples of research work that show that the low surface can have any structure and be arbitrarily complicated. Finally, he explains that res net performs well because of the skip connection it uses to change how gradients flow in the system.
  
  
• 00:50:00 In this section, Dr. Razvan Pascanu talks about some recent results that show the implicit biases in stochastic gradient descent (SGD) and the importance of explicit biases. With regards to SGD, it was traditionally thought that the noise in SGD helped escape sharp minima, but it turns out that there is an implicit bias in the regularizer used in SGD. Additionally, data augmentation noise is hurtful and they found that averaging the gradient over different data augmentations can reduce this noise. Moreover, biases are super important and a slight tweak in the data augmentation can lead to huge improvements in performance. They also explored the idea of different initializations and how they can affect the partitioning of space that is integral in solving the problem. Finally, the use of explicit biases such as pre-training is shown to lead to significant improvements as well.
  
  
• 00:55:00 In this section, Dr. Razvan Pascanu discusses the concept of pre-training and adding inductive biases in deep learning models. He explains that pre-training can help ensure that information can be properly transmitted between nodes and can lead to a significant improvement in exclusive devices. Additionally, Dr. Pascanu describes a unique approach to adding inductive biases by shaping the loss surface rather than adding a regularizer term, which can help the weights become locked to zero and improve the efficiency of learning. He also addresses issues related to catastrophic forgetting and the challenge of decomposing problems in machine learning.
  
  
• 01:00:00 In this section, Dr. Pascanu discusses the idea of forgetting in deep learning and how models are able to recover from it. He suggests that there is still some knowledge hidden even after a model has forgotten certain things, but it is difficult to determine how much knowledge is actually lost. Dr. Pascanu mentions upcoming papers about targeted forgetting, where certain data points are removed from the model to protect privacy, but he believes more research in this area is needed.