[aurareturn]

Are there any books you recommend for deep learning that are written for developers who don't use math every day?

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.

[stefr-]

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 (obvious gross oversimplification).

[lr1970]

The last commit in the repo of "Deep Learning from Scratch" was 5 years ago. It is hopelessly outdated. The field is changing very fast.