Neural Networks

• The Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville
• CS231n: Convolutional Neural Networks for Visual Recognition
• Yann LeCun’s Deep Learning Course at CDS [Home]
• Deep Learning by Yann LeCun & Alfredo Canziani (DS-GA 1008 · SPRING 2020) · NYU CENTER FOR DATA SCIENCE
  • other editions (years) are hosted by Alfredo Canziani
• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges by Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković
• Dive into Deep Learning
• DeepMind x UCL | Deep Learning Lecture Series 2020
• Neural Networks and Deep Learning by Michael Nielsen
• Christopher Olah’s Posts on Neural Networks
• Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
• Algorithms of Reinforcement Learning by Csaba Szepesvári
• Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka, Yuxi Hayden Liu and Vahid Mirjalili (includes sections on Transformers, GANs, GCNs and RL)
• labml.ai Annotated PyTorch Paper Implementations - Multi-Headed Attention, Transformer Encoder and Decoder Models, Denoising Diffusion Probabilistic Models, Wasserstein GAN
• Deep Learning & Applied AI @Sapienza - Course material, 2nd semester a.y. 2023/2024, Dept. of Computer Science taught by Emanuele Rodolà
• fast.ai—Making neural nets uncool again
• Fast.AI Deep Learning For Coders—36 hours of lessons for free

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• karpathy/min-char-rnn.py - Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
• A Recipe for Training Neural Networks

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• Annotated Bibliography of Recommended Materials from the Center for Human-Compatible AI
• Open Learning by Frederik Kratzert