cern channels axis channels-first convention channels-last convention character-level neural language model ciresandan class activationvisualizing heatmaps of - classes classification cloudrunning jobs in clustering cnns see convnets (convolutional neural networkscntk (microsoft cognitive toolkit compilation step concept vectorsfor editing images - conditioning data connect buttonec control panel connectionsresidual - content loss conv layer conv layer convnets (convolutional neural networks - combining with recurrent neural networks - overview - convolution operations - max-pooling operations - processing sequences with - training on small datasets - building networks - data preprocessing - relevance for small-data problems - using data augmentation - using pretrained convnets - feature extraction - fine-tuning - index visualizing convnet learning - convnet filters - heatmaps of class activation - intermediate activations - convnets filters convolution base - convolution operations - border effects - convolution strides paddling - convolution strides convolutions - depthwise separable - cortescorinna crossentropy cuda drivers cudnn library curvature data augmenting - feature extraction with - feature extraction without - batches of - generating sequence data heterogeneous homogenous learning representations from - missing preparing - for character-level lstm text generation for recurrent neural networks - preprocessing - - redundancy representations for neural networks - tensors data batches - examples of data tensors licensed to higher-dimensional tensors image data - key attributes of tensors - manipulating tensors in numpy matrices ( tensors - scalars ( tensors sequence data - timeseries data - vector data vectors ( tensors video data shuffling splitting tokenizing - transformations transforming vectorization data augmentation data distillation data points data representativeness data tensorsexamples of datasetsassembling - dcgans (deep convolutional gansoverview training - decision boundaries decision trees - deep convnets deep learning - accomplishments of - achievements of - democratization of enabling technologies - future of - - automated machine learning - lifelong learning - long-term vision - models as programs - modular subroutine reuse - geometric interpretation of - hardware and - investment in - limitations of - local generalization vs extreme generalization -