percyliang/cs229t

Slides and Jupyter notebooks for the Deep Learning lectures at M2 Data Science Universit Paris Saclay

Lectures for INFO8006 - Introduction to Artificial Intelligence, ULige

Bartosz Milewski’s ‘Category Theory for Programmers’ unofficial PDF and LaTeX source

Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning

Course materials for Georgia Tech CS 4650 and 7650, “Natural Language”

A minimal & modern LaTeX template for your (bachelor’s | master’s | doctoral) thesis

A Tufte-inspired LaTeX class for producing handouts, papers, and books

This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent.

Hacks for training RL systems from John Schulman’s lecture at Deep RL Bootcamp (Aug 2017)