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)
12
Index

Chapter 3. Autoencoders

In the previous chapter, Chapter 2, Deep Neural Networks, you were introduced to the concepts of deep neural networks. We're now going to move on to look at autoencoders, which are a neural network architecture that attempts to find a compressed representation of the given input data.

Similar to the previous chapters, the input data may be in multiple forms including, speech, text, image, or video. An autoencoder will attempt to find a representation or code in order to perform useful transformations on the input data. As an example, in denoising autoencoders, a neural network will attempt to find a code that can be used to transform noisy data into clean ones. Noisy data could be in the form of an audio recording with static noise which is then converted into clear sound. Autoencoders will learn the code automatically from the data alone without human labeling. As such, autoencoders can be classified under unsupervised learning algorithms...