What this book covers
Chapter 1, Introducing Advanced Deep Learning with Keras, covers the key concepts of deep learning such as optimization, regularization, loss functions, fundamental layers, and networks and their implementation in Keras. This chapter also serves as a review of both deep learning and Keras using sequential API.
Chapter 2, Deep Neural Networks, discusses the functional API of Keras. Two widely-used deep network architectures, ResNet and DenseNet, are examined and implemented in Keras, using functional API.
Chapter 3, Autoencoders, covers a common network structure called autoencoder that is used to discover the latent representation of the input data. Two example applications of autoencoders, denoising and colorization, are ...
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