mini-batch mini-batch sgd (mini-batch stochastic gradient descent minskymarvin mnist dataset model checkpointing model class model depth model plot model fit(function model fit_generator(function modelcheckpoint callbacks models architecture patterns - batch normalization - depthwise separable convolution - residual connections - - as layers - as programs - defining developing achieving statistical power - determining capacity ensembling - evaluating - hyperparameter optimization - language sampling from - training - loading glove embeddings in machine learningrisk of anthropomorphizing - multi-input - multi-output - regularizing - training - using keras callbacks - using tensorboard - modular subroutinesreusing - modulesinception - momentum moore' law index mse (mean squared error multiclass classifications - multihead networks multi-input models - multilabel classification multimodal inputs multi-output models - classes naive bayes algorithm naive_add national institute of standards and technology see nist ndim attribute nervana systems neural layers neural networks anatomy of - layers - loss functions models - optimizers binary classifications - breakthroughs in data preprocessing for - handling missing values value normalization - vectorization data representations for - tensors data batches - examples of data tensors higher-dimensional tensors image data - key attributes of tensors - manipulating tensors in numpy matrices ( tensors - scalars ( tensors sequence data - timeseries data - licensed to vector data vectors ( tensors video data gradient-based optimization - backpropagation algorithm - derivatives - gradients stochastic gradient descent - keras - cntk developing with - tensorflow theano multiclass classifications - regression - setting up workstations - gpus for deep learning - jupyter notebooks running jobs in cloud running keras tensor operations - broadcasting - dot - element-wise - geometric interpretation of - geometric interpretation of deep learning - reshaping - neural style transfer - content loss in keras - style loss - -grams nist (national institute of standards and technology non-linearity function non-machine learningbaselines - nonstationary problems normalizing batches - normalizing values - numpy arrays numpy librarymanipulating tensors in numpy matrix numpy tensors nvidia