Recent AI, Big Data, Deep Learning, Machine Learning Info Digest 2017/10/26

AlphaGo Zero: Learning from scratch
In the paper, published in the journal Nature, deepmind team members demonstrate a significant step towards this goal.

Reimplementation of the system described in the Alpha Go Zero paper
For all intents and purposes, it is an open source AlphaGo Zero.

All the Linear Algebra You Need for AI
The purpose of this notebook is to serve as an explanation of two crucial linear algebra operations used when coding neural networks: matrix multiplication and broadcasting.

IEEE VIS 2017: Best Papers and Other Awards
This first part covers the opening, which included presentations of the best papers from all three tracks plus a new Test of Time award category.

Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent
Not surprisingly, many think the talent shortage won’t be alleviated for years.

Are too many people training to become data scientists?
Definitely not. In fact, there is a major shortage of analytical talent across the board. 

Recent AI, Big Data, Deep Learning, Machine Learning Info Digest 2017/10/13

Artificial intelligence can say yes to the dress
The technology, developed by Vue.ai’s Anand Chandrasekaran and Costa Colbert, uses a machine learning approach called generative adversarial networks, or GANs. 

The History of Deep Learning — Explored Through 6 Code Snippets
In this article, we’ll explore six snippets of code that made deep learning what it is today. We’ll cover the inventors and the background to their breakthroughs. Each story includes simple code samples on FloydHub and GitHub to play around with.

China’s AI Awakening
The West shouldn’t fear China’s artificial-intelligence revolution. It should copy it.

Interview: Yoshua Bengio, Yann Lecun, Geoffrey Hinton
October 10, 2017 for the first time ever, RE•WORK brought together the ‘Godfathers of AI’ to appear not only at the same event, but on a joint panel discussion. At the Deep Learning Summit in Montreal, we saw Yoshua Bengio, Yann LeCun and Geoffrey Hinton come together to share their most cutting edge research progressions as well as discussing the landscape of AI and the deep learning ecosystem in Canada.

Deep RL Bootcamp - Lectures
August 2017   |   Berkeley CA

The Data Scientist's Guide to Apache Spark
This repo contains notebook exercises for a workshop teaching the best practices of using Spark for practicing data scientists in the context of a data scientist’s standard workflow. By leveraging Spark’s APIs for Python and R to present practical applications, the technology will be much more accessible by decreasing the barrier to entry.