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Paper Bot: Automated Highlights from a Database of 474 Papers
Open Article April 16th, 2015

Paper Bot: Automated Highlights from a Database of 474 Papers

The @AiGameDev Twitter bot automatically posts interesting highlights from research papers gathered from recent CIG and AIIDE conferences. It uses some simple image-based techniques to find things to post, powered by Python and libraries such as scikit-image and scipy. Find out how it was done and look at some examples...

Take-Aways and High-Level Analysis from GDC 2015
Premium Coverage April 15th, 2015

Take-Aways and High-Level Analysis from GDC 2015

This recording digs into most important ideas and techniques in Game AI from GDC last month, including the AI Summit, as well as big trends take-aways from the conference. You'll hear about the AI, animation and procedural generation in games such as Sunset Overdrive, Watch_Dogs, Galak-Z, and more.

How To Train Your Neural Network: A Python Guide
Premium Tutorial April 4th, 2015

How To Train Your Neural Network: A Python Guide

Using Python is one of the simplest ways to get started with deep learning and neural networks. This tutorial will show you how you can get up and running with PyLearn2 via a simple wrapper library, and the various issues and questions you need to be considering in practice to get things working efficiently and reliably.

A Flanking Opportunity in The AI Sandbox
Premium Release March 28th, 2015

A Flanking Opportunity in The AI Sandbox

Keen to experiment with tactical pathfinding? The source code for the flanking tutorial is now available, along with bonus access to The AI Sandbox. You'll find a grid-based tactical pathfinder as well as one based on a corridor map, written in Python with extensive comments.

Deep Gabe: Machine Learning from Steam Data with Neural Networks
Premium Presentation March 17th, 2015

Deep Gabe: Machine Learning from Steam Data with Neural Networks

Over the past few years, deep learning has revolutionized the technology sector by providing significantly better results on classification or prediction problems that were previously impractical for machine learning. What are the benefit for game developers? What's the catch? This presentation is a practical introduction to deep neural networks, using millions of public player profiles and thousands of games ...