The Hundred-Page Machine Learning Book

What Hacker News says

1. teleforceMay 2025
   Nobody is expecting you to be able to derive and write automatic differentiation (AD) library from scratch but it's always good to know the fundamentals [1].

   Andriy Burkov has written excellent trilogy books series on AI/LLMs namely "The Hundred-Page Machine Learning Book" and "Machine Learning Engineering" and the latest "The Hundred-Page Language Models Book" [2],[3],[4].

   Having said that, the capability of providing useful AI/LLMs solutions for intuitive and interactive learning environment, training portal, standards documentation exploration, business and industry rules and regulations checking, etc based on the open-source local-first data repository with AI/LLMs are probably the killer application that're truly useful for end users, for examples here [5],[6].

   [1] Automatic differentiation:

   https://en.wikipedia.org/wiki/Automatic_differentiation

   [2] The Hundred-Page Machine Learning Book:

   https://www.themlbook.com/

   [3] Machine Learning Engineering:

   https://www.mlebook.com/wiki/doku.php

   [4] The Hundred-page Language Models Book

   https://www.thelmbook.com/

   [5] Local-first software: You own your data, in spite of the cloud:

   https://www.inkandswitch.com/essay/local-first/

   [6] AI-driven chat system designed to support students in the Introduction to Computing course (ECE 120) at UIUC, offering assistance with course content, homework, or troubleshooting common problems. It serves as an educational aid integrated into the course’s learning environment:

   https://www.uiuc.chat/ece120/chat

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2. visargaJan 2021
   The Hundred-Page Machine Learning Book by Andriy Burkov - is quite highly regarded and to the point.

   http://themlbook.com/

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3. dhanainmeOct 2020
   "The Hundred-Page Machine Learning Book" will teach you what ML is. It can be a treated as a light weight replacement for say an Andrew NG course. You wont learn anything practical / code something in PyTorch or Tensorflow but you ll understand what happens under the hood in these framework

   "Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" is an amazing introduction to implementing all ML ideas. I think there is a new PyTorch version. The accompanying notebook will get you started to a point where you can do some hobby projects.

   This book surprisingly seems to fill an interesting gap explaining about how these ML systems are used in real life in large scale. I work for 1 of the FAANG companies & I can say that every chapter here would correspond to bread & butter of a team responsible for maintaining a large ML system say Recommendation / Fraud detection. The target audience would be someone who is interested to learn how to put a large end-end ML system together.

   I would be very excited if there are practical examples on how to use this with MLFlow / KubeFlow / Sagemaker. Really excited to read this

   edit : typos

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4. teleforceSep 2020
   For Machine Learning theory related to Data Science I'd highly recommend "The Hundred-Page Machine Learning Book" by Andriy Burkov [1].

   According to the author, by reading one hundred pages of the book (plus some extra bonus pages), you will be ready to build complex AI systems, pass an interview or start your own business.

   It is a read first book and buy later if you think it is good enough.

   [1] http://themlbook.com/

   thread
5. losvedirMar 2019
   Hmm, so I'm reading The Hundred Page Machine Learning Book [0] which gives an overview of the field, and one of the sections is on convolutional neural networks, and it was very crisp and clear (like the whole book, really; highly recommended). I understand since it's a brief summary it's probably eliding stuff, but it's so much shorter and more understandable than this blog post that it makes me wonder if it's really eliding a lot of crucial stuff, or if this post is just... not clear.

   In the book, it walks you through the convolution of an input matrix with a smaller filter matrix. You start with lining up the smaller matrix in the top left corner of the input matrix, and you multiply each overlapping square together and add them all up. That's the value of the top left element of the result (the result is a matrix). Then you slide the filter to the right, repeat the calculation, and that's the value one to the right in the result matrix. You repeat, panning and scanning across the input matrix and in this fashion fill in the the whole result matrix.

   I thought I had it, but this blog post just confused things. I do remember learning about convolutions back in undergrad and being confused then, too, so that sounds about right. When I read the ML handbook, I had a "wait, is that it?" thought and was confused why I was confused back in the day. But I guess that's just one particular way of doing it, or one use case or something.

   [0] https://www.amazon.com/Hundred-Page-Machine-Learning-Book/dp...

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6. dwrodriFeb 2019
   I did bot have a good experience with Goodfellow’s Deep Learning Book. It’s much more written for the scholar than the practitioner. Furthermore, a couple relatively important methods (Transformer Layers, Causal convolutions, etc.) aren’t given proper treatment because the field was still moving fast at the time of publishing.

   A much better investment would be the One Hundred Page Machine Learning Book. It’s written with practitioners in mind. Link here: https://www.amazon.com/Hundred-Page-Machine-Learning-Book/dp...:

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