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