Book Image

Hands-On Deep Learning Architectures with Python

By : Yuxi (Hayden) Liu, Saransh Mehta
Book Image

Hands-On Deep Learning Architectures with Python

By: Yuxi (Hayden) Liu, Saransh Mehta

Overview of this book

Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: The Elements of Deep Learning
5
Section 2: Convolutional Neural Networks
8
Section 3: Sequence Modeling
10
Section 4: Generative Adversarial Networks (GANs)
12
Section 5: The Future of Deep Learning and Advanced Artificial Intelligence

Recurrent Neural Networks

In this chapter, we will explain one of the most important deep learning models, recurrent neural networks (RNNs). We will start by reviewing what RNNs are and why they are well-suited for processing sequential data. After briefing the evolution paths of RNN models, we will illustrate a variety of RNN architectures that have been categorized by the different forms of input and output data, along with industrial examples. We will figure out the answers to questions such as, how can we generate only one output?, Can output be a sequence?, and does it work with only one input element?

We will follow this by discussing several architectures categorized by the recurrent layer. First, we will apply the basic RNN architecture to write our own War and Peace. The RNNs with vanilla architecture are not good at preserving long-term dependent information...