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)


This chapter provided an overview of the three deep learning models – MLPs, RNNs, CNNs – and also introduced Keras, a library for the rapid development, training and testing those deep learning models. The sequential API of Keras was also discussed. In the next chapter, the Functional API will be presented, which will enable us to build more complex models specifically for advanced deep neural networks.

This chapter also reviewed the important concepts of deep learning such as optimization, regularization, and loss function. For ease of understanding, these concepts were presented in the context of the MNIST digit classification. Different solutions to the MNIST digit classification using artificial neural networks, specifically MLPs, CNNs, and RNNs, which are important building blocks of deep neural networks, were also discussed together with their performance measures.

With the understanding of deep learning concepts, and how Keras can be used as a tool with them, we are now equipped to analyze advanced deep learning models. After discussing Functional API in the next chapter, we'll move onto the implementation of popular deep learning models. Subsequent chapters will discuss advanced topics such as autoencoders, GANs, VAEs, and reinforcement learning. The accompanying Keras code implementations will play an important role in understanding these topics.