Recent AI, Big Data, Deep Learning, Machine Learning Info Digest 2017/08/18

Thoughts after taking the Deeplearning.ai courses
The fast AI course mainly teaches you the art of driving while Andrew’s course primarily teaches you the engineering behind the car.

DeepMind and Blizzard open StarCraft II as an AI research environment
DeepMind's scientific mission is to push the boundaries of AI by developing systems that can learn to solve complex problems. To do this, we design agents and test their ability in a wide range of environments from the purpose-built DeepMind Lab to established games, such as Atari and Go.

Hype or Not? Some Perspective on OpenAI’s DotA 2 Bot
As I started looking more into what exactly the DotA 2 bot was doing, how it was trained, and what game environment it was in, I came to the conclusion that it’s an impressive achievement, but not the AI breakthrough the press would like you to believe it is. That’s what this post is about.

Artificial Intelligence Is Likely to Make a Career in Finance, Medicine or Law a Lot Less Lucrative
First generation robots worked in factories. Second generation robots are preparing for white-collar professions. Sort of like people.

How to create a Neural Network in JavaScript in only 30 lines of code
In this article Per Harald Borgen will show you how to create and train a neural network using Synaptic.js, which allows you to do deep learning in Node.js and the browser.

Finding chairs the data scientist way! (Hint: using Deep Learning) – Part I
In this article, Faizan Shaikh will cover how I defined the problem. I will also mention what were the steps I took to solve the problem. Consider it as a raw uncut version of my experience as I tried to solve the problem.

Recent AI, Big Data, Deep Learning, Machine Learning Info Digest 2017/08/11

ICML 2017 tutorial
34th International Conference on Machine Learning

Deep Reinforcement Learning, Decision Making, and Control
In this tutorial, we will cover the foundational theory of reinforcement and optimal control as it relates to deep reinforcement learning, discuss a number of recent results on extending deep learning into decision making and control, including model-based algorithms, imitation learning, and inverse reinforcement learning, and explore the frontiers and limitations of current deep reinforcement learning algorithms.

Deep Learning for Health Care Applications: Challenges and Solutions
In this tutorial, we will discuss a series of problems in health care that can benefit from deep learning models, the challenges as well as recent advances in addressing those. We will also include data sets and demos of working systems.

Sequence-To-Sequence Modeling with Neural Networks
Sequence-To-Sequence (Seq2Seq) learning was introduced in 2014, and has since been extensively studied and extended to a large variety of domains. Seq2Seq yields state-of-the-art performance on several applications such as machine translation, image captioning, speech generation, or summarization. In this tutorial, we will survey the basics of this framework, its applications, main algorithmic techniques and future research directions.

Interpretable Machine Learning
In this talk, we first suggest a definitions of interpretability and describe when interpretability is needed (and when it is not). Then we will review related work, all the way back from classical AI systems to recent efforts for interpretability in deep learning. Finally, we will talk about a taxonomy for rigorous evaluation, and recommendations for researchers. We will end with discussing open questions and concrete problems for new researchers.

Deep Learning with Andrew Ng
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.


Heroes of Deep Learning: Andrew Ng interviews

Geoffrey Hinton

Yoshua Bengio

Pieter Abbeel

Ruslan Salakhutdinov

Ian Goodfellow

Andrej Karpathy

Recent AI, Big Data and Machine Learning Info Digest 2017/04/02

RBC Innovation & Entrepreneurship Speaker Series with Geoffrey Hinton

Matroid can watch videos and detect anything within them

Google AI Secretly Uploaded to the Internet, Where it Wreaked Havoc on Gamers

What are the largest inefficiencies in a data scientist’s workflow?

Big brands sign on with government-backed AI institute

Canada’s AI Moment

Elon Musk launches Neuralink, a venture to merge the human brain with AI

Canada’s Future Bets on AI

What are Andrew Ng's most notable contributions in the field of artificial intelligence?

Try Deep Learning in Python now with a fully pre-configured VM

Recent Big Data and Machine Learning Info Digest 2016/12/04

The Top 7 Big Data Trends for 2017

Why Self-Service Prep Is a Killer App for Big Data

First 12 chapters of the Machine Learning Yearning book draft from Andrew Ng

An Interactive Tutorial on Numerical Optimization

Probabilistic Programming

Computers could soon be our best developers