contents running deep-learning jobs in the cloudpros and cons what is the best gpu for deep learning classifying movie reviewsa binary classification example the imdb dataset preparing the data building your network validating your approach using trained network to generate predictions on new data further experiments wrapping up classifying newswiresa multiclass classification example the reuters dataset preparing the data building your network validating your approach generating predictions on new data different way to handle the labels and the loss the importance of having sufficiently large intermediate layers further experiments wrapping up predicting house pricesa regression example the boston housing price dataset preparing the data building your network validating your approach using -fold validation wrapping up summary of machine learning fundamentals four branches of machine learning supervised learning unsupervised learning self-supervised learning reinforcement learning evaluating machine-learning models trainingvalidationand test sets keep in mind things to data preprocessingfeature engineeringand feature learning data preprocessing for neural networks engineering overfitting and underfitting feature reducing the network' size adding weight regularization adding dropout the universal workflow of machine learning defining the problem and assembling dataset choosing measure of success deciding on an licensed to