Deep learning resources

A curated list of high-quality resources that complement the implementations and documentation in this hub.

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Core deep learning texts

• Deep Learning (Goodfellow, Bengio, Courville)
  https://www.deeplearningbook.org/ – The definitive textbook.

• Pattern Recognition and Machine Learning (Bishop)
  https://www.microsoft.com/en-us/research/people/cmbishop/

• Neural Networks and Deep Learning (Michael Nielsen)
  http://neuralnetworksanddeeplearning.com/

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Research papers

• Backpropagation: Rumelhart, Hinton & Williams (1986).
• Adam Optimizer: Kingma & Ba (2014) – https://arxiv.org/abs/1412.6980
• Batch Normalization: Ioffe & Szegedy (2015) – https://arxiv.org/abs/1502.03167
• Dropout: Srivastava et al. (2014) – https://jmlr.org/papers/v15/srivastava14a.html

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Online courses

• Deep Learning Specialization (Andrew Ng, Coursera)
  https://www.coursera.org/specializations/deep-learning

• Fast.ai Practical Deep Learning for Coders
  https://course.fast.ai/

• CS231n: Convolutional Neural Networks for Visual Recognition (Stanford)
  http://cs231n.stanford.edu/

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Tools and frameworks

• TensorFlow – https://www.tensorflow.org/
• PyTorch – https://pytorch.org/
• Keras – https://keras.io/

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Supplementary resources

• Distill.pub (Intuitive Explanations) – https://distill.pub/
• The Illustrated Transformer (Jay Alammar) – http://jalammar.github.io/illustrated-transformer/
• Weights & Biases – Experiment Tracking – https://wandb.ai/site

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Contributing resources

To recommend an additional high-quality resource, follow the instructions in the repository's contributing guide.

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