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The Math of Neural Networks
There are many reasons why neural networks fascinate us and have captivated headlines in recent years. They make web searches better, organize photos, and are even used in speech translation. Heck, they can even generate encryption. At the same time, they are also mysterious and how exactly do they accomplish these things ? What goes on inside a neural network? On a high level, a network learns just like we do, through trial and error. This is true regardless if the network is supervised, unsupervised, or semi-supervised. Once we dig a bit deeper though, we discover that a handful of mathematical functions play a major role in the trial and error process. It also becomes clear that a grasp of the underlying mathematics helps clarify how a network learns. In the following chapters we will unpack the mathematics that drive a neural network. To do this, we will use a feedforward network as our model and follow input as it moves through the network.
187 pages, Kindle Edition
Published September 7, 2017
386 people are currently reading
563 people want to read
About the author
Michael Taylor
491Ìýbooks18ÌýfollowersMichael Taylor majored in English at Bloomsburg University, Pennsylvania, and also earned his Master’s degree in Education from the same university.
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Displaying 1 - 18 of 18 reviews
October 7, 2017
A neural network operates similar to the brain’s neural network
Author Michael Benson offers the following before starting his book � ‘This book is designed as a visual introduction to the math of neural networks. It is for BEGINNERS and those who have minimal knowledge of the topic.� For REAL beginners it is helpful to find some definitions of neural networks before beginning this intense course. For example, from the dictionary we learn ‘In information technology, a neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks -- also called artificial neural networks -- are a variety of deep learning technologies. Commercial applications of these technologies generally focus on solving complex signal processing or pattern recognition problems. A neural network is a series of algorithms that attempts to identify underlying relationships in a set of data by using a process that mimics the way the human brain operates. Neural networks have the ability to adapt to changing input so the network produces the best possible result without the need to redesign the output criteria. The concept of neural networks is rapidly increasing in popularity in the area of developing trading systems.�
Now, with a bit of very basic information such as offered above, this book is a definitive exploration and teaching guide as to how to learn neural networking, the facets of it that require concentration to ingest, and the fascination this new form of processing information addresses. Benson makes the reading as accessible as possible with ample illustrations to allow our eyes to organize the concepts into knowledge and understanding.
This is not a quick read self-help book but rather a course on a subject about which many of us are uninformed. It takes time and energy to reach the end of the book, but it is very worthy time well spent.
The verbiage that makes Benson’s teaching is displayed in the following definition: ‘Artificial Intelligence (AI) is an area within computer science that aims to develop machines capable of imitating intelligent human behavior. Additional Details: Neural networks are part of what is called Deep Learning, which is a branch of machine learning. The goal of Deep Learning is to move machine learning towards artificial intelligence. Machine learning is the science of getting computers to act without being explicitly programmed.
General definitions section at the end of the book may just be a fine starting point for most who are absolutely new to this concept. Burt however you elect to jump into this arena the results are rewarding: it is great to gain a clue as to how communication is changing!
Author Michael Benson offers the following before starting his book � ‘This book is designed as a visual introduction to the math of neural networks. It is for BEGINNERS and those who have minimal knowledge of the topic.� For REAL beginners it is helpful to find some definitions of neural networks before beginning this intense course. For example, from the dictionary we learn ‘In information technology, a neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks -- also called artificial neural networks -- are a variety of deep learning technologies. Commercial applications of these technologies generally focus on solving complex signal processing or pattern recognition problems. A neural network is a series of algorithms that attempts to identify underlying relationships in a set of data by using a process that mimics the way the human brain operates. Neural networks have the ability to adapt to changing input so the network produces the best possible result without the need to redesign the output criteria. The concept of neural networks is rapidly increasing in popularity in the area of developing trading systems.�
Now, with a bit of very basic information such as offered above, this book is a definitive exploration and teaching guide as to how to learn neural networking, the facets of it that require concentration to ingest, and the fascination this new form of processing information addresses. Benson makes the reading as accessible as possible with ample illustrations to allow our eyes to organize the concepts into knowledge and understanding.
This is not a quick read self-help book but rather a course on a subject about which many of us are uninformed. It takes time and energy to reach the end of the book, but it is very worthy time well spent.
The verbiage that makes Benson’s teaching is displayed in the following definition: ‘Artificial Intelligence (AI) is an area within computer science that aims to develop machines capable of imitating intelligent human behavior. Additional Details: Neural networks are part of what is called Deep Learning, which is a branch of machine learning. The goal of Deep Learning is to move machine learning towards artificial intelligence. Machine learning is the science of getting computers to act without being explicitly programmed.
General definitions section at the end of the book may just be a fine starting point for most who are absolutely new to this concept. Burt however you elect to jump into this arena the results are rewarding: it is great to gain a clue as to how communication is changing!
November 17, 2024
This book is a good refresher for those who already have a background in math.
October 20, 2017
This is an excellent stand-alone book, though if you are a complete novice to the basics of what a neural network is and how it works, I would recommend first reading Taylor’s other book, “Make Your Own Neural Network: An In-depth Visual Introduction For Beginners� which will walk you through creating your first neural network, step by step, down to explaining each line of code (using Python, but no prior knowledge is assumed).
If you have at least a vague idea already of what a neural network is and how it works even if just in principle, then by all means dive right into this book which focusses, as the title suggests, on the mathematics of neural networks.
However! This is not a dry maths textbook, and the explanations keep it tied closely to the topic at hand with examples aplenty, and in the same style as Taylor’s other work, very lucid step-by-step visual walkthroughs of everything, explaining each part quickly but without assuming prior mathematical knowledge (beyond perhaps the basest of concepts; nothing that should challenge anyone who has even just a high-school level understanding of maths, or perhaps even a not quite that).
All in all, this is an incredibly accessible and illuminating book that I highly recommend to any who have an interest in the under-the-hood aspects of machine learning.
If you have at least a vague idea already of what a neural network is and how it works even if just in principle, then by all means dive right into this book which focusses, as the title suggests, on the mathematics of neural networks.
However! This is not a dry maths textbook, and the explanations keep it tied closely to the topic at hand with examples aplenty, and in the same style as Taylor’s other work, very lucid step-by-step visual walkthroughs of everything, explaining each part quickly but without assuming prior mathematical knowledge (beyond perhaps the basest of concepts; nothing that should challenge anyone who has even just a high-school level understanding of maths, or perhaps even a not quite that).
All in all, this is an incredibly accessible and illuminating book that I highly recommend to any who have an interest in the under-the-hood aspects of machine learning.
January 26, 2018
Not a light read
So I’m not sure what I thought I would learn from this book! It’s definitely not for beginners! You need to have a working understanding of what a neural network is to fully understand and use this book to its fullest potential. I’m honestly not there yet. Prior to this book I had the basic understanding of mathematics and neural networks, I will come back to this book st a later time to get more out of it. It’s very in depth and Tons of links to explain in further depth as it goes along. But if you’re not mathematically minded be prepared to feel completely lost! I feel like this book would be excellent for math and science nerds, this isn’t light reading, it’s heavy and intense on mathematical side of things. If neural networks are a thing you want to know more about this is a good book to keep on hand for reference as you progress through your learning curve.
So I’m not sure what I thought I would learn from this book! It’s definitely not for beginners! You need to have a working understanding of what a neural network is to fully understand and use this book to its fullest potential. I’m honestly not there yet. Prior to this book I had the basic understanding of mathematics and neural networks, I will come back to this book st a later time to get more out of it. It’s very in depth and Tons of links to explain in further depth as it goes along. But if you’re not mathematically minded be prepared to feel completely lost! I feel like this book would be excellent for math and science nerds, this isn’t light reading, it’s heavy and intense on mathematical side of things. If neural networks are a thing you want to know more about this is a good book to keep on hand for reference as you progress through your learning curve.
October 6, 2017
A good evaluation and explanation
Micheal Taylor has done a good job with researching and discussing that research in an easy to follow and understand method. There were a couple of times when I had to read a section twice but the overall collection and presentation of the material is very good. If you are interested in the neural network and how it works and learns , this is a good place to start.
Micheal Taylor has done a good job with researching and discussing that research in an easy to follow and understand method. There were a couple of times when I had to read a section twice but the overall collection and presentation of the material is very good. If you are interested in the neural network and how it works and learns , this is a good place to start.
August 26, 2019
Great start for beginners
I have been finding hard to understand the basics of neural networks through scattered sources from YouTube, Medium, Blogs etc., But always I never felt I understood fully from them. And this book is the only source which took me right through the fundamentals step by step with practical examples and giving me a relief that finally I learnt the basics of neural networks mathematics.
I have been finding hard to understand the basics of neural networks through scattered sources from YouTube, Medium, Blogs etc., But always I never felt I understood fully from them. And this book is the only source which took me right through the fundamentals step by step with practical examples and giving me a relief that finally I learnt the basics of neural networks mathematics.
September 17, 2022
Puts our brains firmly in place but not sure how realistic this is in realation to others. I know some people who approach life through math, they're not particularly mathematical but logic prevails at all times. Basically, we need to value how we learnt to count in those early years because once you can apply counting to things, everything falls into place.
March 23, 2023
This was a great explanation of the basics of how a neural network actually works. It is not a list of applications and praise but the nuts and bolts of how it is put together, how it works, and how one goes about tuning it. There are tons of references for each piece. I did have to reread a lot of it after finishing to firm up my understanding but it wasn't due to the presentation.
November 13, 2020
Wonderful book really clearly explaining the topic
This book really makes the maths of neural network accessible. Each step is broken down to its individual components and its all put together in a very clear walk-through
This book really makes the maths of neural network accessible. Each step is broken down to its individual components and its all put together in a very clear walk-through
November 23, 2020
Good, but not for absolute beginners
The book contains formulas, but without simpler examples to clarify the topic.
If it starts with explaining simple regression formula, then upgrade the knowledge step by step it could be way better.
The book contains formulas, but without simpler examples to clarify the topic.
If it starts with explaining simple regression formula, then upgrade the knowledge step by step it could be way better.
November 30, 2020
Excellent for Absolute Beginners
Excellent for absolute beginers with no computer sciences and mathematics background. Everything explained in plaine english. Had fun reading the book.
Excellent for absolute beginers with no computer sciences and mathematics background. Everything explained in plaine english. Had fun reading the book.
January 7, 2020
Great intro to the math behind neural networks, formulae explained term by term
April 29, 2020
A picture is worth a thousand words
This book introduces one to Neural Networks in a very elementary yet elegant way and prepared one for the battle with more rigorous texts.
This book introduces one to Neural Networks in a very elementary yet elegant way and prepared one for the battle with more rigorous texts.
November 29, 2022
Information ugh
Really didn't do much for me, not great. Info but not put entertainingly... Done in the style of the ou numeracy books that would work...
Really didn't do much for me, not great. Info but not put entertainingly... Done in the style of the ou numeracy books that would work...
November 29, 2023
I think they hit ctrl+p on the web page and didn’t reformat at all
March 16, 2024
I can recommend this book if you want to learn the basics of neural network and its math. The author also uses vidualizations, which is nice.
Two thumbs up!
Two thumbs up!
October 9, 2017
The Math of Neural Networks: A Visual Introduction for Beginners by Michael Taylor is a comprehensive book that details and explains neural networking. It is more than advisable to have done some research on neural networks prior to reading this book, as it is a complex subject and requires a basic understanding. This book is not to be read in one short sitting, but digested slowly, and re-read to fully comprehend the subject matter at hand. Taylor has done a good job of breaking down the information, and if you are a novice, has simplified the language and presentation somewhat to make it more accessible. Anyone who wants to learn more about Deep learning and artificial intelligence will benefit from reading this book. Highly recommend.
December 6, 2017
If you have some knowledge about Neural Networks, and you need to get a handle on the mathematics, then this book is it. The author has made the maths accessible, and generally easy to follow. Loads of diagrams, illustrations and examples that take you by the hand through the maze of mathematical constructs that move neural networks.
And, even if he makes the maths accessible, he maintains the rigour.
Highly recommended for those interested in the maths that drive neural networks.
On my 5-star rating: normally I reserve 5 stars for books that stand out as literary giants; however, the 5 stars here is based on evaluating the book within its genre. As a technical book, it is excellent. I now have a much clearer understanding of the maths of neural networks, and neural networks in general.
And, even if he makes the maths accessible, he maintains the rigour.
Highly recommended for those interested in the maths that drive neural networks.
On my 5-star rating: normally I reserve 5 stars for books that stand out as literary giants; however, the 5 stars here is based on evaluating the book within its genre. As a technical book, it is excellent. I now have a much clearer understanding of the maths of neural networks, and neural networks in general.
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