Deep Learning from Scratch
Source: https://www.oreilly.com/library/view/deep-learning-from/9781492041405/ ↗
Build-to-understand, one layer below the language model.
Weidman constructs neural networks from first principles in plain Python — the maths, the computation and the intuition developed together — from multilayer nets to CNNs and RNNs, then shows how it all maps onto PyTorch.
Where Raschka builds a language model, Weidman builds the machinery beneath it: what a layer, a gradient and backpropagation actually are.
For anyone who learns by taking things apart, it turns the primitives every model rests on from black boxes into things you have made yourself.
Central argument
A from-first-principles construction of neural networks in Python: it develops the mathematics, the computation and the mental model together, building multilayer networks, convolutional and recurrent networks by hand before mapping them onto PyTorch. The thesis is understanding by construction — nothing is a black box if you built it.
Critique
Its scope is neural-network foundations up to about 2019: no transformers, no LLMs. It is deliberately narrow and hand-built, which is its value and its limit — pair it with Raschka for the language-model layer and Goodfellow for the deeper theory.
Why it matters for product
For a CTO/CPO who learns by taking things apart, this is the 'build to understand' rung one level below the LLM: it demystifies the primitives — layers, gradients, backprop — that every model, frontier or local, is ultimately made of. Own these and the higher-level books stop being magic.
- Hands-On Large Language Models — Jay Alammar & Maarten Grootendorst
- Build a Large Language Model (From Scratch) — Sebastian Raschka
- Deep Learning with Python — François Chollet
- Man-Computer Symbiosis — J.C.R. Licklider
- The Rise of AI Search: Implications for Information Markets and Human Judgement at Scale — Sinan Aral, Haiwen Li & R. Zuo
- Beyond Human-Readable: Rethinking Software Engineering Conventions for the Agentic Development Era — Dmytro Ustynov