x_train = x_train.astype('float32') / 255. x_test = x_test.astype('float32') / 255. x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:]))) x_test = x ...

Git isn't hard to learn, and when you combine Git and GitHub, you've just made the learning process significantly easier. This two-hour Git and GitHub video tutorial shows you how to get started with ...

Abstract: Despite advancements using graph neural networks (GNNs) to capture complex user-item interactions, challenges persist due to data sparsity and noise. To address these, self-supervised ...

Abstract: Recent advances in explainable machine learning have highlighted the potential of sparse autoencoders in uncovering mono-semantic features in densely encoded embeddings. While most research ...

prob_drop_input = 0.2 # drop probability for dropout @ input layer prob_drop_hidden = 0.5 # drop probability for dropout @ fc layer ...