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Hands-On Deep Learning Architectures with Python

Hands-On Deep Learning Architectures with Python

By : Yuxi (Hayden) Liu, Mehta
4.5 (2)
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Hands-On Deep Learning Architectures with Python

Hands-On Deep Learning Architectures with Python

4.5 (2)
By: Yuxi (Hayden) Liu, 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)
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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

InfoGANs

InfoGANs (short for Information Maximizing Generative Adversarial Networks) are somewhat similar to CGANs in the sense that both generator networks take in an additional parameter and the conditional variable, c, such as label information. They both try to learn the same conditional distribution, P(X |z, c). InfoGANs differ from CGANs in the way they treat the conditional variable.

CGANs consider that the conditional variable is known. Hence, the conditional variable is explicitly fed into the discriminator during training. On the contrary, InfoGANs assume that the conditional variable is unknown and latent, which we need to infer based on the training data. The discriminator in an InfoGAN is responsible for deriving the posterior, P(c |X). The architecture of an InfoGAN is presented in the following diagram:

Since we do not need to supply the conditional...

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