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Twenty years on from Deep Blue vs Kasparov: how a chess match started the big data revolution On the seventh move of the crucial deciding game, black made what some now consider to have been a critical error. When black mixed up the moves for the Caro-Kann defence, white took advantage and created a new attack by sacrificing a knight. In just 11 more moves, white had built a position so strong that black had no option but to concede defeat. The loser reacted with a cry of foul play – one of the most strident accusations of cheating ever made in a tournament, which ignited an international conspiracy theory that is still questioned 20 years later.
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The Unreasonable Effectiveness of Recurrent Neural Networks There’s something magical about Recurrent Neural Networks (RNNs). I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simple your model is to the quality of the results you get out of it blows past your expectations, and this was one of those times.
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