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

What are RNNs?

Recall that in deep feedforward networks, autoencoder neural networks, and CNNs, which we discussed in the previous chapters, data flows one-way from the input layer to the output layer. However, deep learning models allow data to proceed in any direction—even circle back to the input layer—and are not limited to feedforward architectures. Data looping back from the previous output becomes a part of the next input data. RNNs are great examples of this. The general form of RNNs is depicted in the following diagram, and we will be working on several variants of RNNs throughout this chapter:

As we can see in the preceding diagram, data from previous time points goes into the training of the current time point. The recurrent architecture makes the models work well with time series (such as product sales, stock price) or sequential inputs (words in articles...