Deep Reinforcement Learning: the guide

From Grokking Deep Reinforcement Learning


Playing Games with Tree Search

From Deep Learning and the Game of Go by Max Pumperla and Kevin Ferguson

This article, from Deep Learning and the Game of Go, discusses tree search algorithms and their relevance to various types of games.

What does Deep Learning Contribute to Search

From Deep Learning for Search by Tommaso Teofili

If you’ve ever worked on designing, implementing or configuring a search engine, you’ve faced the problem of having a solution that adapts to your data; deep learning helps provide solutions to these problems which are accurately based on the data, not on fixed rules or algorithms.

Build Your Own Go-Playing Machine

From Deep Learning and the Game of Go


By Max Pumperla and Kevin Ferguson

Deep Learning for R Users


By François Chollet with J. J. Allaire

Dive into Keras


By Dan Van Boxel

Improving Search Performance


By Tommaso Teofili

Go as a Machine Learning Problem

From Deep Learning and the Game of Go by Max Pumperla and Kevin Ferguson

This article is an excerpt from chapter 2 of the upcoming MEAP Deep Learning and the Game of Go. We’re going to look at the game of Go and discuss why it’s such a good subject and learning tool for machine learning and deep learning. Plus, we’ll also discover why games in general are such a good subject for AI as well as which aspects of game playing can be solved with machine learning.

Deep Learning for Text  

From Deep Learning with Python by François Chollet

In this article, we’ll learn about deep learning models that can process text (understood as sequences of word or sequences of characters), timeseries, and sequence data in general.

Introducing Keras: deep learning with Python

By François Chollet

This article introduces Keras, a deep learning library for Python that can be used with Theano and TensorFlow to build almost any sort of deep learning model.

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