Disclaimer: None of the course material are mine. These materials were found after exhaustively searching the internet and what seemed to be free and relevant to the work we do. Note: The directories of the individuals are people from the Lindert Lab who showed interest in working through this crash course with me. For the interested individuals I created a directory (per person with their first name). The created directories for the respective individuals to upload their tutorials, examples, notes, assignments, papers and/or problems they want to talk about.
- https://jovian.ai/learn/deep-learning-with-pytorch-zero-to-gans/lesson/lesson-1-pytorch-basics-and-linear-regression (This covers the basics of pytorch, can be skimmed through super fast.)
- https://jovian.ai/learn/deep-learning-with-pytorch-zero-to-gans/lesson/lesson-2-working-with-images-and-logistic-regression
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- This tutorial is more fun and covers the following:
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- Working with images in PyTorch (using the MNIST dataset).
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- Splitting a dataset into training, validation, and test sets.
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- Creating PyTorch models with custom logic by extending the nn.Module class.
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- Interpreting model outputs as probabilities using Softmax and picking predicted labels.
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- Picking a useful evaluation metric (accuracy) and loss function (cross-entropy) for classification problems.
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- Setting up a training loop that also evaluates the model using the validation set.
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- Testing the model manually on randomly picked examples.
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- Saving and loading model checkpoints to avoid retraining from scratch.
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- For lecture and reading material, please go through this:
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- https://www.youtube.com/watch?v=QyFrYUCXbgI (lecture).
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- https://www.deeplearningbook.org/contents/mlp.html (this is the related chapter for the lecture).