Creating A Chess AI using Deep Learning

When Gary Kasparov was dethroned by IBM's Deep Blue chess algorithm, the algorithm did not use Machine Learning, or at least in the way that we define Machine Learning today. This article aims to use Neural Networks to create a successful chess AI, by using Neural Networks, a newer form of machine learning algorithms. Using a chess dataset with over 20,000 instances (contact at victorwtsim@gmail.com for dataset), the Neural Network should output a move, when given a chess-board. These libraries are the prerequisites to create the program: os and pandas are to access the dataset, python-chess is an "instant" chess-board to test the neural network. Numpy is necessary to perform matrix manipulation.