Part 1

Use this category for discussions of Practical Deep Learning for Coders (2018), Part 1. Be sure to check the FAQ before posting, and read about how to ask for help.

Part 1 Beginner

This category is strictly for discussing topics that we have brought up in class, questions about running the code that we’ve looked at in class, or any other issues related to the lessons we’ve covered already.

Part 2 & Alumni

Use this category for discussions of Cutting Edge Deep Learning for Coders (2018), Part 2. Also use this category if you’ve finished the course and want to discuss topics that may be of interest to other part 2 students and alumni. (For topics that aren’t mentioned in the course, like RL, tensorflow, etc, and for general interest topics such as AI news, please use the #deep-learning forum.)

Deep Learning

Use this category to discuss anything to do with deep learning that’s not related to a fast.ai course (each of those has its own category) - including stuff that’s not related to fast.ai at all! Topics could include new papers, projects, applications, recent news, or anything else you’re interested in!

Computational Linear Algebra

This category is for questions and discussions related to the fast.ai course, Computational Linear Algebra. This blog post introduces the course. Here is advice on how to ask for help in a way that maximizes the chances someone else will be able to provide a helpful answer.

Part 1 (2017)

Please use this category for all discussions of part 1 of the older keras-based course17.fast.ai. (Note that we recommend switching to the new course if possible. The forum for the new course is here: #part1-v2 ).

Part 2 (2017)

Thank you for joining Deep Learning Part 2! Hopefully you've already familiarized yourself with the forums during part 1 of the course, so this will all be very familiar to you.

Site Feedback

Discussion about this site, its organization, how it works, and how we can improve it.

fastai dev

We’re doing a rewrite of the fastai library, with the following goals: