Machine Learning with Neural Networks: An In-depth Visual Introduction with Python: Make Your Own Neural Network in Python: A Simple Guide on Machine Learning with Neural Networks.

excllent well written book on a complex topic

This makes a great intro to learning about Python, machine learning, and neural networks. It is written in a very easy to read and understandable way that even I could understand.I’m a complete beginner to programming and computer mechanics but am starting to learn more and more and am really interested in the field. I found this book super helpful, it cleared up all my questions and explained everything in such a clear, simple fashion.Some of the maths and stuff on gradients I found pretty confusing at first but after reading it through a couple of times I’ve now got the hang of them.If you’ve just started out using Python, and want to learn more about the maths behind neural networking then I’d strongly recommend this guide. It definitely helped me.

Fantastic reference and information. Covers the basics without overwhelming your thinking process.

This is a handy, if long winded, book for those (like me) whose mathematical prowess is lacking, but want to understand how basic neural networks work at a fundamental level. If I can gain an understanding of the concepts of feed forward, activation functions, loss functions, back propagation, partial derivatives - then surely anyone can. My main gripe is that there are an awful lot of silly errors which suggests an issue with its reviewing. Still recommend it though if you're a mathematical duffer.

Excellent book to start with. Clearly explained, even the math, with easy to follow diagrams.

after barely using the book is falling to pieces.

Despite what it says at the top of the page, this book is NOT 'Make Your Own Neural Network.' That is another book entirely, and by a different author. This book's name is 'Neural Networks' - same subject but different title. I suspect Michael Taylor may be trying to attract searches for the name of the other (very successful) book in order to gain sales for his own book? Immoral.

O livro é ótimo para iniciantes por estar disposto passo a passo, mesmo na parte complexa da rede neural, e de forma gráfica.

Non essendo mai addentrato in questo argomento, sebbene da tempo avessi desiderato farlo, volevo saperne di più. E' senz'altro il libro più indicato per muovere i primi passi con le reti neurali e per imparare qualcosa per chi, come me, ha poco tempo. Molto semplice e chiara sia la parte matematica che la parte della programmazione. E non è affatto facile rendere facilmente comprensibile in questo modo un argomento così complesso. Sono state volutamente omesse alcune cose (per chi vuole approfondire ci sono altri libri di testo più avanzati), ma c'è tutto l'essenziale!

Make Your Own Neural Network: An In-depth Visual Introduction For Beginners written by Michael Taylor is a well put together book for beginners interested in Neural Networking. The author does a terrific job at explaining the topics in easy to understand language, with links to more information if you are still unsure as to what is being discussed. There are a lot of skills needed such as mathematics knowledge such as algebra and stats, all of which can be found through the provided links to free courses on the subject matter. By the end of the book, the reader will have a much better understanding of the workings of neural networks and how to create one. The author explains programming in an easy to understand way that even someone with no knowledge of the subject will be able to come away from this book with a much better understanding. I was very impressed by this book and highly recommend it to anyone interested in programming or would like a glimpse into that world.

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

I bought this book to learn about artificial intelligence techniques as a side project from my current work.I found Michael's book very well written also the book itself lacks some little things like a bio of the author or pagination.If you're weak in math, Michael will do its best to explain some very important concepts like the summation operator or partial derivative. If you have trouble understanding those concepts, the first half of the book will be tough to go through but once you arrive at the Python tutorial, it should be easier.I don't recommend this book if you have absolutely no knowledge in mathematics or in programming, but if you know a little bit of both and no absolutely nothing about machine learning algorithms, then go for it.Everything is very easily and well explained!