HandsOn Mathematics for Deep Learning
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Section 1: Essential Mathematics for Deep Learning

Linear Algebra

Vector Calculus

Probability and Statistics

Optimization

Graph Theory

Section 2: Essential Neural Networks

Linear Neural Networks

Feedforward Neural Networks

Regularization

Convolutional Neural Networks

Recurrent Neural Networks

Section 3: Advanced Deep Learning Concepts Simplified

Attention Mechanisms

Generative Models

Transfer and Meta Learning

Geometric Deep Learning

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About 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, multilayered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build fullfledged 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.
 Publication date:
 June 2020
 Publisher
 Packt
 Pages
 364
 ISBN
 9781838647292