Hacker's Guide to Neural Networks

Thanks for the upvotes! I'm a little conflicted about this being linked around too much because it is still very much work in progress. I work on this guide on a side because I think there's a lot of interest in these models out there, and not enough explanations. On a related note, some of you might also be interested in a Stanford CS class I'll be co-teaching with my adviser next quarter. It will be focused primarily on Convolutional…

I've read so many of these, none of them include the information I need. If someone wrote a "Hackers guide to Tuning Hyperparameters" or "Hackers guide to building models for production" I would ready/share the shit out of those.

Since we're on the topic of tutorials to understand neural nets and modern deep learning, I will throw in Michael Nielsen's excellently written free online "book" on neural nets. It's really a set of 6 long posts that gets you from 0 to understanding all of the fundamentals with almost no prerequisite math needed. Using clear and easy to understand language, Michael explains neural nets, the backprop algorithm, challenges in training these models, some commonly used modern building blocks and…

Some more good posts when it was submitted last year: https://news.ycombinator.com/item?id=8553307