Name: Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks
SKU: 1956
Price: 19.98 USD
Availability: InStock
Rating: 5 (4 reviews)

A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures

Key Features

Understand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networks
Learn the mathematical concepts needed to understand how deep learning models function
Use deep learning for solving problems related to vision, image, text, and sequence applications

Book Description

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.

What you will learn

Understand the key mathematical concepts for building neural network models
Discover core multivariable calculus concepts
Improve the performance of deep learning models using optimization techniques
Cover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizer
Understand computational graphs and their importance in DL
Explore the backpropagation algorithm to reduce output error
Cover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)

Who this book is for

This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.

Linear Algebra
Vector Calculus
Probability and Statistics
Optimization
Graph Theory
Linear Neural Networks
Feedforward Neural Networks
Regularization
Convolutional Neural Networks
Recurrent Neural Networks
Attention Mechanisms
Generative Models
Transfer and Meta Learning
Geometric Deep Learning

4 reviews for Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks

TD59

Rated 5 out of 5

February 24, 2021

5.0 out of 5 stars Great resource for Machine Learning

I teach Machine Learning in graduate school. Many times, students ask me for a book that provides a quick refresh of key math principles. While there ...More
I teach Machine Learning in graduate school. Many times, students ask me for a book that provides a quick refresh of key math principles. While there are many great books to choose from, I usually put Hands-on Mathematics high on my list.

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Kabeer

Rated 5 out of 5

October 9, 2020

5.0 out of 5 stars Great, simple introductory text

The book is a good introductory text to get into the subject. Really clarifies concepts in a very simple and intuitive way. Recommended in particular ...More
The book is a good introductory text to get into the subject. Really clarifies concepts in a very simple and intuitive way. Recommended in particular for anyone who has struggled with math in the past too.

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Amazon Customer

Rated 5 out of 5

October 9, 2020

5.0 out of 5 stars Worth a read

This book does a great job of breaking things down for those who do not have a deep knowledge in mathematics. The author tries to break down and simpl...More
This book does a great job of breaking things down for those who do not have a deep knowledge in mathematics. The author tries to break down and simplify concepts to make them intuitive for readers who are trying to break into the field. This book explains things in a clear and simple manner. If you’re looking to get into the field of deep learning, I would highly recommend this reading this book.

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Andrew B.

Rated 5 out of 5

September 17, 2020

5.0 out of 5 stars Very informative and easy to understand

I have read many books on deep learning, and many of them talk about theory where it is beyond the reach of the average reader who doesn't have a stro...More
I have read many books on deep learning, and many of them talk about theory where it is beyond the reach of the average reader who doesn't have a strong math background of a MS in the sciences, or is about programming them in some language or library, but they very rarely break things down and explain them the way this book does. While the book may lack mathematical rigour and deep explanations and proofs, it makes a very good attempt at trying to simplify concepts to make them intuitive for readers who are trying to get into the field and don't come from a strong mathematical background. On the cover, the book claims to cover a breadth of topics but it seems to be focused on the main classes of neural networks (FNNs, CNNs, RNNs) and gives brief introductions to the other topics. I had struggled with many concepts like backpropagation in recurrent and convolutional nets, but this book explained it very well and very simply. If you’re looking to get into the field of deep learning, I highly recommend this reading this book.

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Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks

Key Features

Book Description

What you will learn

Who this book is for

Table of Contents

4 reviews for Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks

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Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks

Key Features

Book Description

What you will learn

Who this book is for

Table of Contents

4 reviews for Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks

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Quick links

Frequently bought with Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks