Book Overview & Buying
Table Of Contents

Hands-On Mathematics for Deep Learning

By : Dawani

3.5 (10)

Buy this Book

Hands-On Mathematics for Deep Learning

3.5 (10)

By: Dawani

Buy this Book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Section 1: Essential Mathematics for Deep Learning

Free Chapter

Linear Algebra

Comparing scalars and vectors

Linear equations

Matrix operations

Vector spaces and subspaces

Linear maps

Matrix decompositions

Summary

Vector Calculus

Single variable calculus

Multivariable calculus

Vector calculus

Summary

Probability and Statistics

Understanding the concepts in probability

Essential concepts in statistics

Summary

Optimization

Understanding optimization and it's different types

Exploring the various optimization methods

Exploring population methods

Summary

Graph Theory

Understanding the basic concepts and terminology

Adjacency matrix

Types of graphs

Graph Laplacian

Summary

Section 2: Essential Neural Networks

Linear Neural Networks

Linear regression

Polynomial regression

Logistic regression

Summary

Feedforward Neural Networks

Understanding biological neural networks

Comparing the perceptron and the McCulloch-Pitts neuron

MLPs

Training neural networks

Deep neural networks

Summary

Regularization

The need for regularization

Norm penalties

Early stopping

Parameter tying and sharing

Dataset augmentation

Dropout

Adversarial training

Summary

Convolutional Neural Networks

The inspiration behind ConvNets

Types of data used in ConvNets

Convolutions and pooling

Working with the ConvNet architecture

Training and optimization

Exploring popular ConvNet architectures

Summary

Recurrent Neural Networks

The need for RNNs

The types of data used in RNNs

Understanding RNNs

Long short-term memory

Gated recurrent units

Deep RNNs

Training and optimization

Popular architecture

Summary

Section 3: Advanced Deep Learning Concepts Simplified

Attention Mechanisms

Overview of attention

Understanding neural Turing machines

Exploring the types of attention

Transformers

Summary

Generative Models

Why we need generative models

Autoencoders

Generative adversarial networks

Flow-based networks

Summary

Transfer and Meta Learning

Transfer learning

Meta learning

Summary

Geometric Deep Learning

Comparing Euclidean and non-Euclidean data

Graph neural networks

Spectral graph CNNs

Mixture model networks

Facial recognition in 3D

Summary

Other Books You May Enjoy

Leave a review - let other readers know what you think

Feedforward Neural Networks

In the previous chapter, we covered linear neural networks, which have proven to be effective for problems such as regression and so are widely used in the industry. However, we also saw that they have their limitations and are unable to work effectively on higher-dimensional problems.

In this chapter, we will take an in-depth look at the multilayer perceptron (MLP), a type of feedforward neural network (FNN). We will start by taking a look at how biological neurons process information, then we will move onto mathematical models of biological neurons. The artificial neural networks (ANNs) we will study in this book are made up of mathematical models of biological neurons (we will learn more about this shortly). Once we have built a foundation, we will move on to understanding how MLPs—which are the FNNs—work and their involvement with...