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Comment by Cieric | Hacker News Reader

First thing I thought of when I read the title was sentdex's nnfs.io (neural networks from scratch.) I don't have time to look at this in full right now, are there any major differences between the these? From my quick glance over this looks like it's heavy on the math notation and when it gets to the code it's already getting to complex (for someone who doesn't know python) to read.

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The approach to this book is very similar to nnfs.io (a similar focus on deriving models from the bare bones). The biggest difference is that his focuses on deep learning while mine covers a) a wider range of models and b) more introductory methods (linear regression, logistic regression, naive bayes, etc.)

It is definitely heavy on the math notation. Since the purpose of the book is to provide mathematical derivations, it's impossible to do that without lots of notation. That's why I added the notation and conventions page (right after the table of contents). If you do get a chance to look through it and see any notation in particular that is difficult to follow, please let me know and I'll update that page.

As for the code, knowing Python is a loose pre-requisite, but familiarity with object oriented programming in general should be enough to at least follow along. Most of the code does one of two things: 1) manipulates data represented as vectors/matrices or 2) creates/manipulates the objects themselves. But I should work on making the commenting clearer so it's apparent what each step does, even for someone less comfortable with Python.

Thanks so much for your feedback!

Thanks for the summary, the object oriented stuff is fine in my mind. The trouble with the code comes from things like the numpy functions. I have experience in matrices for what's used in computer graphics, but some of numpys functions are conflicting with what I understand those constructs to do. I don't know maybe it's the lack of any type information that makes this hard for me, I can't just look at the something I know and figure out what is going on from there, I have to go through each function one by one to make sure I know what it actually returns.

Either way, good luck with the book. I've considered doing knowledge dumps myself for game/game engine oriented information but I don't even know where I would start with that.

Thanks for the feedback. Sounds like you're not alone in your thoughts on numpy. I'll brainstorm better solutions--maybe explaining each numpy function in a side note or adding a numpy overview to the appendix. It just makes things so much easier so I am hesitant to go numpy-less.

Getting started was definitely the hardest part. I put the book together with JupyterBook (https://jupyterbook.org/intro.html), which I would highly recommend! Really neat tool and a helpful community.

Looks like this one is free and also covers a wider breadth of ML topics (though perhaps doesn't cover NNs in as much detail -- I don't have a paid copy of nnfs).

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