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Math for Deep Learning: A Practitioner's Guide to Mastering Neural Networks
Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits.
With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning.
You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network.
In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.
With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning.
You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network.
In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.
344 pages, Paperback
Published July 6, 2021
56 people are currently reading
194 people want to read
About the author
Ronald T. Kneusel
17 books29 followersMy infatuation with computers began in 1981 with an Apple II. I've been active in machine learning since 2003, and deep learning since before AlexNet was a thing.
My background includes a Ph.D. in computer science from the University of Colorado, Boulder (deep learning), and an M.S. in physics from Michigan State University.
By day, I work in industry building deep learning systems. By night, I type away on my keyboard generating the books you see here. I sincerely hope that if you explore my books, you gain as much enjoyment from them as I had in creating them.
Please see my full list of books here: www.rkneusel.com
Questions? Comments? Let me know at rkneuselbooks@gmail.com
My background includes a Ph.D. in computer science from the University of Colorado, Boulder (deep learning), and an M.S. in physics from Michigan State University.
By day, I work in industry building deep learning systems. By night, I type away on my keyboard generating the books you see here. I sincerely hope that if you explore my books, you gain as much enjoyment from them as I had in creating them.
Please see my full list of books here: www.rkneusel.com
Questions? Comments? Let me know at rkneuselbooks@gmail.com
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Community Reviews
Displaying 1 - 5 of 5 reviews
October 20, 2021
1. Why do I need this work?
To lay groundwork for understanding the mathematical groundwork for Deep Learning.
2. Why should I care about Math?
Well -- you could speak another language, by investing yourself to speaking a new language, Mathematics.
One would establish formal jargon to speak Deep Learning.
Would you want to represent problems in n-dimensions?
Would you want to solve problems in high-dimensional space?
3. So, What's inside this?
0 -Setup
1 -Probability
2 -More Probability
3 -Statistic
4- Linear Algebra
5-Differential Calculus
6-Matrix Calculus Data Flow in Neural Network
7-Data Flow in Neural Network
8- Backpropagation
9-Gradient Descent
Mostly all are fun ways to describe problems, represent them.
4. How much time would this take?
A week.
Reach out to me for notes, summary.
"The World is non-linear"
Deus Vult,
Gottfried
To lay groundwork for understanding the mathematical groundwork for Deep Learning.
2. Why should I care about Math?
Well -- you could speak another language, by investing yourself to speaking a new language, Mathematics.
One would establish formal jargon to speak Deep Learning.
Would you want to represent problems in n-dimensions?
Would you want to solve problems in high-dimensional space?
3. So, What's inside this?
0 -Setup
1 -Probability
2 -More Probability
3 -Statistic
4- Linear Algebra
5-Differential Calculus
6-Matrix Calculus Data Flow in Neural Network
7-Data Flow in Neural Network
8- Backpropagation
9-Gradient Descent
Mostly all are fun ways to describe problems, represent them.
4. How much time would this take?
A week.
Reach out to me for notes, summary.
"The World is non-linear"
Deus Vult,
Gottfried
Read
June 2, 2022Numpy and Scipy transcriptions and versions of 1st and 2nd year-level math techniques (probability, linear algebra, matrix manipulation etc)
August 25, 2022
Provides a good overview of the mathematics and methods behind deep learning technologies. Good high-level overview of mathematical concepts with a surprisingly thorough explanation of each in the short length of the book. A great read, all in all.
December 28, 2022
Very interesting and accessible book to have a refresher in calculus and linear algebra to understand better the mechanics behind deep learning and machine learning in general.
It focuses more on neural networks, giving you a good idea of how to build your own from scratch. The last two chapters are the hardest, but still easy enough to grasp the basics.
Of course, this uses python, but I'm interested in implementing it in Mata/Stata, just to have better understanding of it all.
It focuses more on neural networks, giving you a good idea of how to build your own from scratch. The last two chapters are the hardest, but still easy enough to grasp the basics.
Of course, this uses python, but I'm interested in implementing it in Mata/Stata, just to have better understanding of it all.
April 12, 2025
I've only read a few chapters on linear algebra to refresh my knowledge for a paper I'm reading, but I'm impressed. The text is clear and concise. It also demonstrates concepts using numpy, which is great for a programmer like me. The author also does a great job at leaving out unnecessary information and I believe he chose a great boundary, not too much and not too little.
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I finished the rest of the book.
The author delivers his promise to give the readers just enough knowledge about Maths to understand modern Machine Learning concepts. The explanation is great throughout the book. I'd highly recommend this for technical folks who have dabbled into ML but didn't have great Maths background.
---
I finished the rest of the book.
The author delivers his promise to give the readers just enough knowledge about Maths to understand modern Machine Learning concepts. The explanation is great throughout the book. I'd highly recommend this for technical folks who have dabbled into ML but didn't have great Maths background.
Displaying 1 - 5 of 5 reviews