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

by Jay Dawani (Author)
4.0 4.0 out of 5 stars 54 ratings
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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.

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Previous page
  1. ISBN-10
    1838647295
  2. ISBN-13
    978-1838647292
  3. Publisher
    Packt Publishing
  4. Publication date
    June 12, 2020
  5. Language
    English
  6. Dimensions
    9.25 x 7.52 x 0.76 inches
  7. Print length
    364 pages
  8. See all details
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Editorial Reviews

About the Author

Jay Dawani is a former professional swimmer turned mathematician and computer scientist. At present, he is the founder of Lemurian Labs - a startup that is developing a next generation AI accelerator to enable deep learning on edge devices, as well as a platform for developing enterprise scale autonomous robotics. Previously he was the Director of Artificial Intelligence at Geometric Energy Corporation where he led research efforts and developed various AI based solutions for industry. He has also advised many companies including Spacebit Technology on the development of their Lunar Rover, and SiaClassic Foundation on the development of their decentralized information storage and retrieval platform. He has spent the last three years researching at the frontiers of AI with a focus on computer vision, reinforcement learning, open-ended learning, deep learning, multi-agent and complex systems, and artificial general intelligence.

Product details

  • Publisher ‏ : ‎ Packt Publishing (June 12, 2020)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 364 pages
  • ISBN-10 ‏ : ‎ 1838647295
  • ISBN-13 ‏ : ‎ 978-1838647292
  • Item Weight ‏ : ‎ 1.41 pounds
  • Dimensions ‏ : ‎ 9.25 x 7.52 x 0.76 inches
  • Customer Reviews:
    4.0 4.0 out of 5 stars 54 ratings

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Customer reviews

4 out of 5 stars
4 out of 5
54 global ratings

Top reviews from the United States

Amazon Customer
5.0 out of 5 stars Great Book!
Reviewed in the United States on October 9, 2020
Verified Purchase
I have been a data scientist for several years now and because of the hype I decided to try and learn deep learning. I decided to buy this book and it does a very good job of explaining how the different fields of math such as linear algebra, vector calculus, probability, etc come together to create various neural networks in a very clear and simple manner that anyone can understand. And it gives several good walk-throughs of forward and backward propagation in various NNs and shows comparisons between architectures and possible use cases as well.
4 people found this helpful
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Amazon Customer
5.0 out of 5 stars Worth a read
Reviewed in the United States on October 9, 2020
Verified Purchase
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.
2 people found this helpful
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drewster
2.0 out of 5 stars Very poorly written
Reviewed in the United States on September 27, 2020
Verified Purchase
While the book does cover a broad set of topics, and is a reasonable resource to refresh your memory of various topis and some details, the writing is very poor, with a lot of incorrect and imprecise information. This quote from the book about vector spaces is the furthest from rigor you could possibly get.

"Vector spaces are one of the fundamental settings for linear algebra, and, as the name suggests, they are spaces where vectors reside."

Contrary to the subtitle: "Build a solid mathematical foundation for training efficient deep neural networks" this book will not provide a solid mathematical foundation.
23 people found this helpful
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TD59
5.0 out of 5 stars Great resource for Machine Learning
Reviewed in the United States on February 24, 2021
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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.
3 people found this helpful
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Yalin
3.0 out of 5 stars okay
Reviewed in the United States on April 13, 2021
Verified Purchase
The book is okay. But there are so many typoes, which makes it harder to read.
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Kabeer
5.0 out of 5 stars Great, simple introductory text
Reviewed in the United States on October 9, 2020
Verified Purchase
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.
2 people found this helpful
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Sergio Di Fiore
1.0 out of 5 stars Poor
Reviewed in the United States on December 9, 2020
Verified Purchase
Poorly written, intended to who already have knowledge, jumping from point A to B without any explanation. Not for those who intend self-learn.
5 people found this helpful
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Matthew Emerick
5.0 out of 5 stars Comprehensive, Well Organized, and Practical
Reviewed in the United States on June 29, 2020
About This Book
This book is the latest in a collection of attempts to teach aspiring and experience machine learning practitioners the mathematics underlying their field. Knowing this will help anyone truly understand how their algorithms work in more detail and can assist in strengthening their predictions or troubleshooting when the results don't match up.

It begins with the basics of linear algebra, calculus, and statistics, moves on to optimization and graph theory, and then digs deeper into more specific mathematics. The second section takes the foundation and looks at different kinds of simpler neural networks. The final section pushes even further into deep learning and makes sure that the reader can explain why their deep learning models do what they do.

Who is This For?
From the Preface, this book is for anyone who wants to go from the basic mathematical concepts to the exact mathematical structures used in calculating the results for deep learning. It does not expect the reader to know the math, though it would be helpful, but it does expect the reader to know the basics of how machine learning works.

With how difficult most people find mathematics, I know that many machine learning practitioners won't want to delve deep into these fundamentals, if they even are curious at all. But every field has a foundation, and I would argue that mathematics is that foundation for machine learning. After all, machine learning is fundamentally a mathematically transformation of the input to the output.

Why Was This Written?
There are only a handful of books written on this subject, of which I own most of them. Most focus only on the basics of linear algebra, calculus, and statistics. Another perspective on comparing these books is the approach taken. I can think of only one other book written specifically for the mathematics of deep learning, and that one assumes the reader thinks more like a mathematician. This book, instead, sees the reader as a developer with hands on experience who wants to know why their algorithms work. I feel that this is an important contribution to anyone's collection.

Organization
Three three section organization works well here. The author progresses from the basics to the core subject matter indicated by the title of the book without leaving anything important out. Remember, this book isn't about learning everything about these mathematical subjects, but about knowing enough to better understand deep learning. You're aren't going to focus on proving that the math works, but will trust that the math works and apply it.

Within each chapter, the author deftly introduces each topic, builds on the subject from the basics up to the more advanced subtopics you should know about, and then gives a summary to refresh the earlier material and bring it all together. There is a complete preface, which I recommend you read through before starting, and a further reading section with other offerings from the publisher on deep learning.

Did This Book Succeed?
I think the author did very well. It gives the reader all of the mathematics they will need to progress on their machine learning and deep learning journeys. Where I think this book fell short, as it is written for practitioners, is that it could have expanded by another 100-150 pages and added in code to show how a programming language can efficiently calculate the math. This could help the reader write better code. If the author decides to write a second edition, I hope that they will consider this.

Rating and Final Thoughts
I give this book a 5 out of 5. It is complete, readable, and helpful. It does what it says it is going to do very well. I commend the author and hope to see more from them. If you are considering buying this book, it is a good addition if you want to be a better machine learning practitioner.
10 people found this helpful
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Top reviews from other countries

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priyaranjanmuduli
1.0 out of 5 stars Don't Buy. Very low-quality technical content. Worst book in Deep Learning. An overpriced book.
Reviewed in India on February 6, 2023
Verified Purchase
Don't Buy. Very low-quality technical content. Worst book in Deep Learning. An overpriced book.
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Anonyme
2.0 out of 5 stars Trop approximatif…
Reviewed in France on January 9, 2023
Verified Purchase
Bonne compilation de savoirs, mais trop léger et approximatif pour ceux qui ont deja de bonnes bases en maths et souhaitent approfondir leurs connaissances et compréhensions de certains concepts rencontrés dans des publications scientifiques … Il y a aussi beaucoup d’erreur de typo dans les formules voire des formules erronées (théorie des graphs notamment).
J’ai clairement préféré d’autres ressources à ce livre. Dommage car l’intention et le scope étaient bons…
One person found this helpful
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Steve
2.0 out of 5 stars Omits too many workings-out
Reviewed in the United Kingdom on September 25, 2022
Verified Purchase
I've only read the first chapter (Linear Algebra), so I can't comment on the rest of the book, but if this is an indication of things to come, it's going to be a real struggle. The chapter jumps from one concept to another, and from one complex equation to derived equations and leaves it for the reader to fill the gaps. There are few 'workings out' for interim steps - just summary phrases, such as "By substituting the values back into the previous equation backward (from bottom to top), we can solve, and find that x=2, y=-1 and z=1". Unless you have a good background in mathematics, be prepared to do lots of supplementary reading from other sources, to provide the missing explanations.
One person found this helpful
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