- All the notebooks use PyTorch to as the DL framework.
- Tips for training DL models
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conv_example.py: Python code to train a Convolution NN on custom dataset, using pytorch dataloader. (End2end example)
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GAN.ipynb: Train a DCGAN on CelebA dataset to generate fake faces.
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DCGAN.ipynb: Train a DCGAN on MNIST dataset to generate fake digits.
- Training challenges: -- Use small batch size especially if model is small, large batch size may train Discriminator very fast during early phase of training.
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dataprep_bpe.ipynb: Example notebook to load custom Language dataset for Machine translation and tokenize it using Transformers Library with Byte Pair Encoding.
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transformers.ipynb: Train a custom transformer model for Machine Translation
- reinforce_cartpole.ipynb: REINFORCE algorithm from scratch to solve a simple cartpole envionment. Notebook taken from Yandex RL course