Прекрасное на ночь

Прекрасное на ночь.

https://www.sscardapane.it/alice-book

Book: Alice’s Adventures in a differentiable wonderlandPermalink
Neural networks surround us, in the form of large language models, speech transcription systems, molecular discovery algorithms, robotics, and much more. Stripped of anything else, neural networks are compositions of differentiable primitives, and studying them means learning how to program and how to interact with these models, a particular example of what is called differentiable programming.

This primer is an introduction to this fascinating field imagined for someone, like Alice, who has just ventured into this strange differentiable wonderland. I overview the basics of optimizing a function via automatic differentiation, and a selection of the most common designs for handling sequences, graphs, texts, and audios. The focus is on a intuitive, self-contained introduction to the most important design techniques, including convolutional, attentional, and recurrent blocks, hoping to bridge the gap between theory and code (PyTorch and JAX) and leaving the reader capable of understanding some of the most advanced models out there, such as large language models (LLMs) and multimodal architectures.

Table of contents
1. Foreword and introduction
2. Mathematical preliminaries
3. Datasets and losses
4. Linear models
5. Fully-connected layers
6. Automatic differentiation
7. Convolutive layers
8. Convolutions beyond images
9. Scaling up models
10. Transformer models
11. Transformers in practice
12. Graph layers
13. Recurrent layers
14. Appendix A: Probability theory
15. Appendix B: Universal approximation in 1D

Book draft: https://arxiv.org/abs/2404.17625