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)


In recent years, deep learning has made unprecedented success stories in difficult problems in vision, speech, natural language processing and understanding, and all other areas with abundance of data. The interest in this field by companies, universities, governments, and research organizations has accelerated the advances in the field. This book covers select important advances in deep learning. The advanced theories are explained by giving a background of the principles, digging into the intuition behind the concepts, implementing the equations and algorithms using Keras, and examining the results.

Artificial Intelligence (AI), as it stands today, is still far from being a well-understood field. Deep learning, as a sub field of AI, is in the same position. While it is far from being a mature field, many real-world applications such as vision-based detection and recognition, product recommendation, speech recognition and synthesis, energy conservation, drug discovery, finance, and marketing are already using deep learning algorithms. Many more applications will be discovered and built. The aim of this book is to explain advanced concepts, give sample implementations, and let the readers, as experts in their field, identify the target applications.

A field that is not completely mature is a double-edged sword. On one edge, it offers a lot of opportunities for discovery and exploitation. There are many unsolved problems in deep learning. This translates into opportunities to be the first to market – product development, publication, or recognition. The other edge is that it would be difficult to trust a not completely well-understood field in a mission-critical environment. We can safely say that if asked, very few machine learning engineers will ride an auto-pilot plane controlled by a deep learning system. There is a lot of work to be done to gain this level of trust. The advanced concepts that are discussed in this book have a high chance of playing a major role as the foundation in gaining this level of trust.

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.