AI Saturdays

• What we'll do
We'll be watching and working through Artificial Intelligence/Deep Learning classes as a group! There are excellent free course lectures and materials online, and from January 6th 2018 to April 21 2018 we'll go through all the contents.

In order to cater to a diverse audience, there will be 3 structured sessions every Saturday – you can attend all, some or none, it’s totally up to you! If you don’t want to attend some of the sessions, throughout the day there will be open hacking on creating open-source code implementations of the top research paper pre-prints that week. Or use that time to catch-up on lectures and readings (sessions 2 and 3 have many hardcore readings by the way!) while discussing with peers.

Session 1: Fast.ai i Part 1 (v2) Lesson 1
Session 2: Stat385 Lecture 2 Readings – Overview of Deep Learning
Session 3a: UCL/Deep Mind Reinforcement Learning Lecture 1 – Intro to Reinforcement Learning

Note that Sessions 1 & 2 will begin with the 2nd lecture of their corresponding courses.

The Agenda
Session 1
10:00 AM - 12:00 PM (Beginner-Intermediate)
Practical Deep Learning- Fast.ai Part 1 (v2)
Fast.ai Lesson 1 – http://forums.fast.ai/t/welcome-to-part-1-v2/5787

Break
12:00 PM - 1:00 PM Lunch
Occasional brown bag lunch talk from an expert :)

Session 2
1:00 PM - 3:00 PM (Intermediate-Advanced)
Deep Learning Theory- Stanford STAT385 course on Theories of Deep Learning
https://stats385.github.io/readings
https://stats385.github.io/lecture_slides
Lecture 2- https://www.youtube.com/watch?v=VsBFt_-h5QA&index=2&list=PLhWmdj1YUpdT-UwCLVRNX509hZrKqZ83V

Session 3 (Intermediate-Advanced)
3:00 PM - 6:00 PM
Reinforcement Learning- UCL/DeepMind Reinforcement Learning
Lecture 1 – https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLacBNHqv7n9gp9cBMrA6oDbzz_8JqhSKo

This is the Portland, OR chapter of AI Saturdays https://nurture.ai/ai-saturdays

Below are some resources used by other chapters that are having prep sessions before the the January 6th meeting:

Optional Prep Sessions (self study at home)

For Python Programming Prep
Dataquest for a good guided Python introduction explanations and exercises: (Intro to Python available on free plan) https://www.dataquest.io/

Python refresher + numpy+ matplotlib by Stanford https://github.com/kuleshov/cs228-material/blob/master/tutorials/python/cs228-python-tutorial.ipynb

Learn Python 3 The Hard Way https://learnpythonthehardway.org/python3/

HitchHiker's Guide to Python http://docs.python-guide.org/en/latest/

Pandas tutorials (optional, not as important for AI model building but super important for data science) https://mltrk.io/link/https%3A%2F%2Fbitbucket.org%2Fhrojas%2Flearn-pandas/2mLEMJHdm6eijExEKXHV

For Linear Algebra Prep:
3Blue1Brown Linear Algebra https://www.youtube.com/watch?v=kjBOesZCoqc&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

MIT Linear Algebra https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/

Khan Academy https://www.khanacademy.org/math/linear-algebra

For Git Prep:
https://www.learnenough.com/git-tutorial

Misc Prep:
https://medium.com/ai-saturdays