deep learning with python by françois chollet

A brief history of generative recurrent networks Introduction to TensorBoard: the TensorFlow visualization framework Using callbacks to act on a model during training Inspecting and monitoring deep-learning models using Keras callba- acks and TensorBoard Going beyond the Sequential model: the Keras functional API Combining CNNs and RNNs to process long sequences Understanding 1D convolution for sequence data Using recurrent dropout to fight overfitting A common-sense, non-machine-learning baseline Advanced use of recurrent neural networks Putting it all together: from raw text to word embeddings The relevance of deep learning for small-data problems

Training a convnet from scratch on a small dataset Regularizing your model and tuning your hyperparameters Scaling up: developing a model that overfits Developing a model that does better than a baseline Defining the problem and assembling a dataset The universal workflow of machine learning Data preprocessing, feature engineering, and feature learning Validating your approach using K-fold validation Predicting house prices: a regression example The importance of having sufficiently large intermediate layers A different way to handle the labels and the loss Classifying newswires: a multiclass classification example Using a trained network to generate predictions on new data

Classifying movie reviews: a binary classification example Running deep-learning jobs in the cloud: pros and cons Jupyter notebooks: the preferred way to run deep-learning experiments Loss functions and optimizers: keys to configuring the learning process Layers: the building blocks of deep learning Chaining derivatives: the Backpropagation algorithm Derivative of a tensor operation: the gradient The engine of neural networks: gradient-based optimization A geometric interpretation of deep learning Geometric interpretation of tensor operations The gears of neural networks: tensor operations 3D tensors and higher-dimensional tensors

Before we begin: the mathematical building blocks of neural networks Decision trees, random forests, and gradient boosting machines Before deep learning: a brief history of machine learning Understanding how deep learning works, in three figures Artificial intelligence, machine learning, and deep learning Bibliography Includes bibliographical references and index.

Deep learning with python by françois chollet