Book Image

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Rowel Atienza
Book Image

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Rowel Atienza

Overview of this book

Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
Table of Contents (16 chapters)
14
Other Books You May Enjoy
15
Index

Introducing Advanced Deep Learning with Keras

In this first chapter, we will introduce three deep learning artificial neural networks that we will be using throughout the book. These networks are MLP, CNN, and RNN (defined and described in Section 2), which are the building blocks of selected advanced deep learning topics covered in this book, such as autoregressive networks (autoencoder, GAN, and VAE), deep reinforcement learning, object detection and segmentation, and unsupervised learning using mutual information.

Together, we'll discuss how to implement MLP, CNN, and RNN based models using the Keras library in this chapter. More specifically, we will use the TensorFlow Keras library called tf.keras. We'll start by looking at why tf.keras is an excellent choice as a tool for us. Next, we'll dig into the implementation details within the three deep learning networks.

This chapter will:

  • Establish why the tf.keras library is a great choice to use for advanced deep learning
  • Introduce MLP, CNN, and RNN – the core building blocks of advanced deep learning models, which we'll be using throughout this book
  • Provide examples of how to implement MLP, CNN, and RNN based models using tf.keras
  • Along the way, start to introduce important deep learning concepts, including optimization, regularization, and loss function

By the end of this chapter, we'll have the fundamental deep learning networks implemented using tf.keras. In the next chapter, we'll get into the advanced deep learning topics that build on these foundations. Let's begin this chapter by discussing Keras and its capabilities as a deep learning library.