Make Your Own Neural Network: An In-depth Visual Introduction For Beginners Paperback – October 4, 2017

Author Michael Taylor offers no biographical information to provide a reference for his expertise in writing this book, but begin reading and absorbing this well illustrated manual that is designed for Beginners only (as Michael states, ‘This book is designed as a visual introduction to neural networks. It is for BEGINNERS and those who have minimal knowledge of the topic. If you already have a general understanding, you might not get much out of this book’) and as such it is a solid starting point about a complex subject.Michael’s manner of definition and explanation and teaching is easily accessible and even a pleasure to read. He first defines his subject – ‘Neural networks have made a gigantic comeback in the last few decades and you likely make use of them everyday without realizing it, but what exactly is a neural network? What is it used for and how does it fit within the broader arena of machine learning? To start, we’ll begin with a high-level overview of machine learning and then drill down into the specifics of a neural network…. A neural network, also known as an artificial neural network, is a type of machine learning algorithm that is inspired by the biological brain. It is one of many popular algorithms that is used within the world of machine learning, and its goal is to solve problems in a similar way to the human brain. Neural networks are part of what’s called Deep Learning, which is a branch of machine learning that has proved valuable for solving difficult problems, such as recognizing things in images and language processing. Neural networks take a different approach to problem solving than that of conventional computer programs. To solve a problem, conventional software uses an algorithmic approach, i.e. the computer follows a set of instructions in order to solve a problem. In contrast, neural networks approach problems in a very different way by trying to mimic how neurons in the human brain work. In fact, they learn by example rather than being programmed to perform a specific task. Technically, they are composed of a large number of highly interconnected processing elements (nodes) that work in parallel to solve a specific problem, which is similar to how the human brain works.’Taking us into the meat of the book, Michael informs us, ‘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 mind-bending: 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. This is why the following chapters will be devoted to understanding 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.’This is an intelligent, well-scripted book, rich in helpful diagrams, that makes a topic about which most of us have little knowledge and turns that topic into fresh, useable knowledge. And that is a feat! Grady Harp, September 17

Make Your Own Neural Network by Michael Taylor is an introductory text that is meant to teach people the basics needed to understand what neural networks are, how they function, and the different types. The book begins by taking the reader through the topics that will be discussed. It then moves into a discussion of the types of neural networks and their different uses and components. This section is what is going to be the most useful to people who are new to this topic. It shows the reader lots of terminology that the reader will need to be able to understand the chapters that show up later on in the book. The next major topic that the book dives into is the math behind neural networks. This is a section that lets the reader know that this topic is not for the faint of heart. The math required to be able to work with the networks requires a high level of knowledge of algebra, statistics, and calculus. This section also comes along with charts that will help the reader have a better understanding of the concepts that are in the chapter and later chapters as well. After the opening chapters the book begins to dive into the heavier concepts that make up the content of the book.This book is very informative and will be very useful to anyone who is looking to brush up on the idea of neural networks or looking to be introduced to the idea. I will say that it may be a heavy read for anyone who does not have a background in this topic. I had a hard time keeping up with some of the content and had to reread some sections to be sure I understood what was going on. This is not a knock on the author or on the content. It is just a very in depth subject that requires some background knowledge. The book was well edited and presented in a very easy to follow format. Great job to the author.

This is an excellent book which covers a complex topic such as Neural Networks - I started reading this book with no knowledge of Neural networks, but after reading it, I can say that I do understand the principles of Neural Networks, a second or 3rd read and I can be a probationer/developer/researcher - yes this book is comprehensive on the theory, practicals, tools and processes to get anyone started off on Neural Networks.First of all let us understand the Target Audience - this book is a lot of math, it will work even if you have a good background in Math that is enough to understand the concepts and understand, but if you are a totally from a non-math background, this is not for you. I graduated about 22 years back and totally out of touch with theory, but I was able to understand it, so some background/concept is necessary, but if you are really good in math - this book really goes deep into it.The author does a great job in simplifying the concepts, example a partial derivative is beautifully explained as "enables you a measure how a single variable out of many impacts another single variable" and chain rule is explained as "Discovering the error of the specific weight is an important aspect of training the networks"When I first read the first few chapters of the book, it felt that this was going nowhere - there were concepts around nodes, weights, error ... some of which I was able to understand and some I could not. So but it really all came together when I was on the practical example - a neural network which can read a image and determine if it is a chicken or a man. It explains a simple 64 pixel image, each pixel contains a number which represents the color and based on the color - we should arrive at 0 for chicken and 1 for man. And how we can keep on adjusting weights until we arrive at the right answer and minimize error.Essentially the image is reduced to a single number and that single number is derived by assigning weights assigned to each pixel - to me this was poetic and achieved my NNN (Neural Network Nirvana) 33,000 feet above ground while I read this book on my way back from on an international trip !