Book Image

Hands-On Mathematics for Deep Learning

By : Jay Dawani
Book Image

Hands-On Mathematics for Deep Learning

By: Jay Dawani

Overview of this book

Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL.
Table of Contents (19 chapters)
1
Section 1: Essential Mathematics for Deep Learning
7
Section 2: Essential Neural Networks
13
Section 3: Advanced Deep Learning Concepts Simplified

Generative adversarial networks

The generative adversarial network (GAN) is a game theory-inspired neural network architecture that was created by Ian Goodfellow in 2014. It comprises two networks—a generator network and a critic network—both of which compete against each other in a minimax game, which allows both of them to improve simultaneously by trying to better the other.

In the last couple of years, GANs have produced some phenomenal results in tasks such as creating images that are indistinguishable from real images, generating music when given some recordings, and even generating text. But these models are known for being notoriously difficult to train. Let's now find out what exactly GANs are, how they bring about such tremendous results, and what makes them so challenging to train.

As we know, discriminative models learn a conditional distribution...