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将棋だったらサイコロを振って飛車とかの進めるマス数を決めたりとか、 囲碁やオセロならサイコロを振って2つ石が置けるとか。 そうじゃないと強い人がずっと強いままで、 なんかそういったアットランダムな運要素を加えても面白いと思うんだけど。
Explaining and illustrating orthogonal initialization for recurrent neural networks June 27, 2016 One of the most extreme issues with recurrent neural networks (RNNs) are vanishing and exploding gradients. Whilst there are many methods to combat this, such as gradient clipping for exploding gradients and more complicated architectures including the LSTM and GRU for vanishing gradients, orthogonal
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with
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