Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

• DLFS_code

  1 456 0.0 Jupyter Notebook

  Code for the book Deep Learning From Scratch, from O'Reilly September 2019

> I suppose the goal would be to understand deep learning so that we know enough of what's going on but not to get stuck in math concepts that we probably don't know and won't use.

I am/was in this scenario. I'm sure there are other resources out there specifically aimed at developers, but a book I'm reading now is "Deep Learning From Scratch" by Seth Weidman. He takes a different approach, by explaining concepts in three distinct methods: a mathematical way, by using diagrams and by showing the code.

I like this approach because it allows me to connect the math to the problem, whereas otherwise you wouldn't have.

In the book, you're slowly creating a DL framework, as the title says, from scratch. He also has all the code on GitHub: https://github.com/SethHWeidman/DLFS_code

I think if you are truly trying to understand deep learning, you will never get to avoid the math because that's really what it is at it's core, a couple of non-linear functions chained together.

• Category_Theory_Machine_Learning

  10 1,122 6.7 Python

  List of papers studying machine learning through the lens of category theory

There's also the cats.for.ai group and this nice github: https://github.com/bgavran/Category_Theory_Machine_Learning

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