Advanced Deep Learning with Keras

Preface
Every book in deep learning will not be able to completely cover the whole
field. This book is not an exception. Given the time and space, we could have
touched interesting areas such as detection, segmentation and recognition,
visual understanding, probabilistic reasoning, natural language processing and
understanding, speech synthesis, and automated machine learning. However,
this book believes in choosing and explaining select areas so that readers can
take up other fields that are not covered.
As the reader is about to read the rest of this book, they need to keep in mind that
they chose an area that is exciting and can have a huge impact on the society. We
are fortunate to have a job that we look forward to working on as we wake up in
the morning.
Who this book is for
The book is intended for machine learning engineers and students who would
like to gain a better understanding of advanced topics in deep learning. Each
discussion is supplemented with code implementation in Keras. This book is for
readers who would like to understand how to translate theory into a working code
implementation in Keras. Apart from understanding theories, code implementation
is usually one of the difficult tasks in applying machine learning to real-world
problems.
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 discussed and
implemented in Keras.
Chapter 4, Generative Adversarial Networks (GANs), discusses one of the recent
significant advances in deep learning. GAN is used to generate new synthetic
data that appear real. This chapter explains the principles of GAN. Two
examples of GAN, DCGAN and CGAN, are examined and implemented in Keras.
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