This is a computer hex player based on deep convolutional neural networks, which I've written mostly as an exercise to teach myself about machine learning and tensorflow. The network architecture and search algorithm is basically copied from AlphaGo Zero, but adapted to hex.
Unlike AlphaGo, selfplay games are generated using only the policy network; a game is generated by sampling from the distribution output by the policy network each move, then one position is taken from each game and MCTS is applied to that position to obtain a training example (using the output of MCTS as a target for the policy output and the game result as a target for the value output). This gives a larger number of independent training examples for less computational cost (though lower quality).
This can be played against with HexGui, setting gtp.py as the program. It uses python 3 and requires numpy, keras and tensorflow.