keras framework - cntk developing with - running tensorflow theano keras library keras applications module keras callbacks module keras preprocessing image kernel methods - kernel trick -fold validationiterated with shuffling - kingmadiederik krizhevskyalex regularization regularization label lambda layer language models sampling from - training - last-layer activation latent spaces of imagessampling from - overview layer compatibility layered representations learning layers differentiable directed acyclic graphs of - inception modules - residual connections - freezing models as - overview - recurrent in keras - stacking - unfreezing weight sharing - layer-wise pretraining leakyrelu layer lecunyann lenet network lhc (large hadron collider lifelong learning - linear transformations local generalizationextreme generalization vs - local port forwardingsetting up logistic regression algorithm logreg (logistic regression logs argument lookback parameter lookback timesteps loss function loss plateau loss value lovelaceada lstm (long short-term memory - generating text with - generating sequence data history of generative recurrent networks implementing characterlevel text generation - sampling strategy - overview machine learning automated - basic approaches - branches of - reinforcement learning - self-supervised learning - supervised learning unsupervised learning data preprocessing - deep learning vs - evaluating models of - choosing evaluation protocols test sets - training sets - validation sets - feature engineering - feature learning - licensed to history of - decision trees - gradient boosting machines - kernel methods - neural networks - probabilistic modeling random forests - learning representations from data - modelsrisk of anthropomorphizing - overfitting and underfitting - adding dropout - adding weight regularization - reducing network size - workflow of - - assembling datasets - choosing evaluation protocol choosing measure of success defining problems - developing models - preparing data - regularizing models - tuning hyperparameters - see also non-machine learning mae (mean absolute error matplotlib library matrices ( tensors - maximum operation max-pooling operations - maxpooling layer maxpooling layer mean_squared_error memorization capacity metrics metricslogging microsoft cognitive toolkit see cntk mikolovtomas