contents evaluation protocol preparing your data developing model that does better than baseline scaling updeveloping model that overfits regularizing your model and tuning your hyperparameters part summary deep learning in practice learning for computer vision deep introduction to convnets the convolution operation operation the max-pooling training convnet from scratch on small dataset the relevance of deep learning for small-data problems downloading the data building your network data preprocessing using data augmentation using pretrained convnet feature extraction up fine-tuning visualizing what convnets learn wrapping visualizing intermediate activations visualizing convnet filters visualizing heatmaps of class activation summary learning for text and sequences deep working with text data one-hot encoding of words and characters using word embeddings putting it all togetherfrom raw text to word embeddings wrapping up understanding recurrent neural networks recurrent layer in keras understanding the lstm and gru layers concrete lstm example in keras wrapping up advanced use of recurrent neural networks temperature-forecasting problem preparing the data common-sensenon-machine-learning baseline basic machine-learning approach first recurrent baseline using recurrent dropout licensed to