Highlights in this edition include: lots of implementations of state-of-the-art models such as SPINN, ∂4, Nested LSTMs, Capsule Networks, and Minigo; useful resources for learning matrix calculus or NLP and searching past Kaggle competitions; tutorials that will teach how to build a domain-specific assistant for Google Home, perform object recognition on encryted data, or train a CNN in Google Sheets; articles about RL such as different applications (trading, games, robotics) and its bias-variance trade-off; and as always exciting research papers.