symbols operator operator numerics tensors see scalars convolutions - poolingfor sequence data tensors see vectors tensors see matrices embeddings activation activation functions ad targeting add_loss method admm (alternating direction method of multipliers adversarial networks see also generative deep learninggenerative adversarial networks adversary network affine transformations amazon web services see aws amd analytical engine annotations application program interfaces see functional apis architecture of networks - convnets densely connected networks - recurrent neural networks - architecture patterns of models - batch normalization - depthwise separable convolution - residual connections - - arrow of time artificial intelligence - expectations for history of - arxiv preprint server - assembling datasets - augmented intelligence augmenting data - feature extraction with - feature extraction without - autoencoders see vaes (variational autoencodersautoml systems autonomous driving aws (amazon web servicesgpu instances running jupyter on setting up - using jupyter on licensed to babbagecharles backend enginekeras backpropagation algorithm - backward pass bag-of- -grams bag-of- -grams bag-of-words baidu batch axis batch normalization batchnormalization layer batch_size bengioyoshua bidirectional layers binary classifications - binary crossentropy black boxes blas (basic linear algebra subprograms border effects - broadcasting operations - browserslocalusing jupyter from - callbackswriting - cam (class activation map categorical encoding categorical_crossentropy function