Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Amita Kapoor, Sujit Pal
Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Antonio Gulli, Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
17
Other Books You May Enjoy
18
Index

A note on TensorFlow and automatic differentiation

TensorFlow can automatically calculate derivatives, a feature called Automatic Differentiation. This is achieved by using the chain rule. Every node in the computational graph (see Chapter 2, TensorFlow 1.x and 2.x) has an attached gradient operation for calculating the derivatives of input with respect to output. After that, the gradients with respect to parameters are automatically computed during backpropagation.

Automatic differentiation is a very important feature because you do not need to handcode new variations of backpropagation for each new model of a neural network. This allows quick iteration and running many experiments faster.