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 2. Deep Neural Networks

In this chapter, we'll be examining deep neural networks. These networks have shown excellent performance in terms of the accuracy of their classification on more challenging and advanced datasets like ImageNet, CIFAR10, and CIFAR100. For conciseness, we'll only be focusing on two networks, ResNet [2][4] and DenseNet [5]. While we will go into much more detail, it's important to take a minute to introduce these networks:

ResNet introduced the concept of residual learning which enabled it to build very deep networks by addressing the vanishing gradient problem in deep convolutional networks.

DenseNet improved the ResNet technique further by allowing every convolution to have direct access to inputs, and lower layer feature maps. It's also managed to keep the number of parameters low in deep networks by utilizing both the Bottleneck and Transition layers.

But why these two models, and not others? Well, since their...