Book Image

Advanced Deep Learning with Keras

By : Rowel Atienza
Book Image

Advanced Deep Learning with Keras

By: Rowel Atienza

Overview of this book

Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
Table of Contents (13 chapters)

Chapter 1. Introducing Advanced Deep Learning with Keras

In this first chapter, we will introduce the three deep learning artificial neural networks that we will be using throughout the book. These deep learning models are MLPs, CNNs, and RNNs, which are the building blocks to the advanced deep learning topics covered in this book, such as Autoencoders and GANs.

Together, we'll implement these deep learning models using the Keras library in this chapter. We'll start by looking at why Keras is an excellent choice as a tool for us. Next, we'll dig into the installation and implementation details within the three deep learning models.

This chapter will:

  • Establish why the Keras library is a great choice to use for advanced deep learning
  • Introduce MLPs, CNNs, and RNNs – the core building blocks of most advanced deep learning models, which we'll be using throughout this book
  • Provide examples of how to implement MLPs, CNNs, and RNNs using Keras and TensorFlow
  • 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 models implemented using Keras. In the next chapter, we'll get into the advanced deep learning topics that build on these foundations, such as Deep Networks, Autoencoders, and GANs.