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Ya so far this is the best introduction to neural networks from first principles... | Hacker News

news.ycombinator.com · 572 words · saved by 1 readers

Quickly skimming the draft pdf at https://arxiv.org/pdf/2404.17625 I can grok it instantly, because it's written in familiar academic language instead of gobbledygook. Anyone with an undergrad math education in engineering, computer science, etc or a self-taught equivalent understanding of differential equations should be able to read it easily. It does a really good job of connecting esoteric terms like tensors with arrays, gradients with partial derivatives, Jacobians with gradients and backpropagation with gradient descent in forward/reverse mode automatic differentiation. Which helps the reader to grasp the fundamentals instead of being distracted by the implementation details of TensorFlow, CUDA, etc. Some notable excerpts: Introduction (page 4): I started self-studying programming some time ago, then pivoted to AI/ML and (understandably) ended up mostly studying math, these resources are a boon to my folk.

Ya so far this is the best introduction to neural networks from first principles that I've seen. Quickly skimming the draft pdf at https://arxiv.org/pdf/2404.17625 I can grok it instantly, because it's written in familiar academic language instead of gobbledygook. Anyone with an undergrad math education in engineering, computer science, etc or a self-taught equivalent understanding of differential equations should be able to read it easily. It does a really good job of connecting esoteric terms like tensors with arrays, gradients with partial derivatives, Jacobians with gradients and…

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