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.

by Michael Taylor

Make Your Own Neural Network in Python A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.  What you will gain from this * A deep understanding of how a Neural Network works. * How to build a Neural Network from scratch using Python. Who this book is * Beginners who want to fully understand how networks work, and learn to build two step-by-step examples in Python. * Programmers who need an easy to read, but solid refresher, on the math of neural networks. What’s Inside - ‘Make Your Own Neural An Indepth Visual Introduction For Beginners’ What Is a Neural Network? 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?

we gently explore these topics so that we can be prepared to dive deep further on. To start, we’ll begin with a high-level overview of machine learning and then drill down into the specifics of a neural network.

The Math of Neural Networks 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.

* Forward Propagation * Calculating The Total Error * Calculating The Gradients * Updating The Weights Make Your Own Artificial Neural Hands on Example You will learn to build a simple neural network using all the concepts and functions we learned in the previous few chapters. Our example will be basic but hopefully very intuitive. Many examples available online are either hopelessly abstract or make use of the same data sets, which can be repetitive. Our goal is to be crystal clear and engaging, but with a touch of fun and uniqueness. This section contains the following eight chapters.

Building Neural Networks in Python There are many ways to build a neural network and lots of tools to get the job done. This is fantastic, but it can also be overwhelming when you start, because there are so many tools to choose from. We are going to take a look at what tools are needed and help you nail down the essentials. To build a neural network

Tensorflow and Neural Networks There is no single way to build a feedforward neural network with Python, and that is especially true if you throw Tensorflow into the mix. However, there is a general framework that exists that can be divided into five steps and grouped into two parts. We are going to briefly explore these five steps so that we are prepared to use them to build a network later on. Ready? Let’s begin.

Neural Distinguish Handwriting We are going to dig deep with Tensorflow and build a neural network that can distinguish between handwritten numbers. We’ll use the same 5 steps we covered in the high-level overview, and we are going to take time exploring each line of code.

Genres: Computer Science, Programming, Artificial Intelligence, Nonfiction, Technology, Science, Textbooks

373 pages, Kindle Edition

First published January 1, 2017

About the author

Michael Taylor majored in English at Bloomsburg University, Pennsylvania, and also earned his Master’s degree in Education from the same university.

Reviews

[Amanda · Rating 3 out of 5]

I feel like I should preface this by saying I am the past person you would find reading a tech book. My husband is the IT guy and I'm happy to leave anything computer/technology related to him. In fact I'm beginning to think that's the secret to our marriage but I digress. Going in a novice, this book still managed to teach me something without making me feel like a fool. The writing is simple and explanatory without being boring. Diagrams throughout the book are surprisingly intelligible and uncomplicated. This is unsurprisingly necessary in a book on a topic as complicated as I find this subject matter. Now my husband read through the book and found it to be too elementary for him to enjoy but that's the difference in our levels of prior knowledge. As he said, I doubt many tech experts would find this on their reading list since it is written as a beginners guide. Serving as a beginners guide, I think it's very well written and full of clear, straight-forward information on the nuances of neural networks. The terms section in the back was especially helpful!

[Valery · Rating 4 out of 5]

If you were ever wondering how you were going to build your own neural network, well look now further. This book, Make Your Own Neural Network: An In-Depth Visual Introduction for Beginners by Michael Taylor has all the information you need to tackle this project. While it is probably helpful to know some higher level math to undertake this process, such as calculus, the visual presentation in this book makes the process seemingly easier to understand and very approachable. Virtually every page has an illustration of some kind, which is very helpful. A book that is helpful for beginners just starting out as well as programmers who are looking to refresh their knowledge, this guide covers all the bases. From the beginning stages to Python libraries and everything in between, this guide is super helpful and will guide you on your way. Highly recommend.

[Sofía · Rating 4 out of 5]

muy buen material, dados mis (escasos) conocimientos es exactamente lo que buscaba. He echado un poco de menos más profundidad/detalle en las explicaciones de la parte matemática y me sobraban bastantes aclaraciones de la parte de código. Supongo que una persona con mejor base matemática pero más nueva en programación podría decir lo mismo pero al revés. En general es un manual muy recomendable para comprender mejor el funcionamiento de las redes neuronales en computación

[Alexey · Rating 4 out of 5]

Почти все технологии хорошо объяснимы, когда видно эффект от их работы и есть определенный опыт наблюдения за технологиями. Глядя на автомобиль мы примерно понимаем, как он работает: топливо поджигается, получившиеся испарения приводят в движение поршни, те, в свою очередь приводят в движение колеса. Когда же технологии относительно новы, понимание их принципа действия существенно затруднено: авторы их описывающие уже прошли путь в понимании, но не всегда готовы его передать другим. При этом повседневного опыта с новыми технологиями нет по определению: на то они и новые технологии – примеры их использования либо доступны нам в их «пользовательской версии» (Фейсбук, узнающий друзей на фотографиях) в единичных случаях. Технология созревает руководствами, сообществами практиков, числом людей, которые в состоянии объяснить принцип действия на кухне после пятого бокала.

За последние несколько лет, я делаю второй или третий подход к нейросетям. Я не математик, кандидатскую защищал по другой теме, поэтому ищу для себя максимально упрощенные способы объяснить для себя технологии. Отсюда и Тэйлор с его «иллюстрированными нейросетями для начинающих». Меня просили писать выводы из книг: выводов не будет. Книга предельно прикладная – описывает из чего состоит процесс работы (на примере задачи классификации), объясняет термины и формулы (очень много формул, но объясненных с предельной скрупулезностью). Далее дается пример создания простой сети классификации 8-битного изображения с помощью Tensor Flow. Вывод, наверное, такой: нейросети – круто, дайте все. Или еще: нейросети – не магия, а много вычислений.

Стоит ли читать? Если не эту книгу (эту книгу, думаю, читать не стоит), то точно стоит погружаться в эту тематику. Я абсолютно убежден, что машинное обучение / «пресловутый» ИИ – это прорывная технология, которая резко увеличит производительность труда и резко поменяет технологический уклад в масштабе, который нам сложно еще предположить. Особенно мне импонирует, что нейросети меняют сам принцип мышления о вычислительных задачах. До Интернета люди, время от времени, представляли себе в виде антиутопий и прогнозов глобальную сеть моментальной передачи информации, но ни в одном из предсказаний не могли с должной точностью предсказать того объема изменений, которые подобная технология принесет, для всей планеты. Аналогично с самолетом, автомобилем и т.д.

Поэтому, стоит, стоит, стоит читать о нейросетях, даже если вы не разработчик. Рискну предположить, что очень скоро нейросети дойдут в своем интерфейсе до «массового» уровня, когда зная принципы работы вы сможете решать задачи, которые вы раньше не могли решить или даже не представляли, что они будут решаемыми.

Если книгу читать не соберетсь, то советую посмотреть курс от Гугл (2017) для не-разработчиков

[Bogdan Sabău · Rating 4 out of 5]

great at explaining the math inside a NN

[Lisa · Rating 3 out of 5]

Make Your Own Neural Network: An In-depth Visual Introduction For Beginners, written by Michael Taylor contains some extremely complicated math formulas that are just above my comprehension. I was able to follow along and get a better understanding of how networks function because there are many visual diagrams and formulas that are drawn out to simplify the explanation. Inevitably one will need to know calculus and other higher math to achieve a goal of making your own neural network. It is however well written and each step is explained in detail, then a summary of each step learned to ensure the lesson is understood at the ends of the chapters. The formulas used to calculate the input to arrive at the output are so complex they need unpacking. Good job with this one, unfortunately it was above my learning level.

[Jimmy Jefferson · Rating 4 out of 5]

A technical description of building a neural network

Michael Taylor does a good job of breaking down the neural network by individual pieces then assembling each piece for you as you understand it's place in the network. There are some math formulas in the completion of building a neural network. That may sound like too much but Michael does a great job of breaking down the formula and explaining why it is used and what function it serves. Each part of building a free thinking network is explained in easy to follow steps. You can learn to build an AI network that will learn to make choices based on individual network learning instead of a preset group of algorithms. If you have any interest in neural networks and AI I would recommend reading this one to help further your understanding and knowledge of the process.

[Shanell Meek · Rating 4 out of 5]

Very informative and easy to understand

Before I started this book all of this neural network stuff was wayyy above my head. Michael Taylor did an amazing job of dumbing it down for me and explaining in intricate detail how Neural Networks work. I wasn’t sure if find a book that would teach me how they work and how to build one and make it easy all easy for someone who is sometimes technology challenged to fully understand, but I really did in this book! He lays it all out in layman’s terms so I came away with a decent understanding of Nuero Networking and how it’s beneficial. The way it’s written you can jump around a little bit, but if you aren’t familiar with all the terminology it’s best to read through then go back and jump through because he introduces new terms throughout the book. I’d definitely recommend this to anyone new to the concept of neuro networking.

[Rachel Kester · Rating 4 out of 5]

If you’re looking for a way to learn about neural networks and want to make your own, you’ll definitely want to check out this book. Michael Taylor illustrates through words and pictures how neural networks work and how you can use Python and Tensorflow to help create your own.

The book is 316 pages which means you’ll need to invest a little time into reading it. While it is intended for beginners, it often talks about certain aspects about neural networks that you should be familiar with before reading it. The illustrations included are a helpful visual way for you to learn about the different terms and ideas Taylor presents in the book. Overall, it’s a great book for those interested in computer science and technology. It also includes quite a bit of mathematical formulas in it so if you’re interested in numbers and formulas, you’ll also want to check this book out.

[Kate Brackett · Rating 4 out of 5]

This is the last sort of book I would have ever chosen to read, but it was helpful! I have applied to a few jobs where one of the requirements was experience with "Python." I didn't even know what it was until I read this book. As a complete beginner, the book was well written and easy to understand. It does help if you have a higher level math understanding but I was able to grasp many of the concepts nonetheless. The book taught me about neural networks without being condescending or amateurish. The author knows his stuff and was able to convey the information well. the illustrations were extremely helpful to a complete beginner and they definitely helped me comprehend and retain the information well.

[Istvan Szabo · Rating 4 out of 5]

A very decent reading in case you have enough mathematical background but never ever worked much with neural networks. It will give you a nice theoretical help on how to start your first experimental project and on how to continue further down the road in order to become the next NN-superman. Not necessarily sufficient to become a real-life data scientist with an NN specialization.

[Enrique Mañas · Rating 5 out of 5]

A complete and meticulous guide providing a thorough introduction to Neural Networks - the book claimed to be for beginners, I think it reaches an intermediate level since there are some frameworks also introduced. Remember to polish your algebra, calculus and math skills if they are a bit rusty.

[Jose Gutierrez · Rating 4 out of 5]

Excelente libro

Es una excelente introducción. A diferencia de otros libros introductorios maneja muy bien el balance entre ser demasiado detallado o presentar ejemplos donde uno solamente se queda con el ejemplo sin poder penetrar en los conceptos.

[Seksit Siripongsatat · Rating 5 out of 5]

Strongly recommand

It’s a perfect book for beginners. I’ve read many books related to machine learning. This book is undoubtedly one of the best! It gives a systematic introduction for machine learning. I strongly recommend it to any Machine Learning student.

[Jonathan Jeckell · Rating 4 out of 5]

I need to go through the last couple of chapters again while doing the steps for some of the examples on python.

The first part walks one through how neural networks work while some of the latter parts are more practical with concrete steps to walk you through building one.

[CrunchyApple · Rating 5 out of 5]

A clearly written tutorial for a vanilla neural network by both math and coding.
By far the best and easiest teaching material for a layman with a college-level math background.

Thank you for taking the time to write out and picture out all the information.

[Surya Vamshi · Rating 4 out of 5]

Neural Network explained in simple way, author has done great job in making complex neural networks easy to understand.

[Eduardo Hernandez · Rating 4 out of 5]

Bien en general

Le falta más desarrolló en lo asociado a construcción de modelos en Python. Lo asociado a Tensorflow es poco didáctico.

[Dr. Tathagat Varma · Rating 3 out of 5]

A good step-by-step introduction to building your first neural network.

[Kimmie · Rating 5 out of 5]

This was truly an interesting book. I normally read romance and young adult books. But this book was suggested to me. After reading the synopsis I thought that this book may help me. And I am so glad that I read it. It is so full of insightful information. It is extremely well written and very easy to understand.
The book says it's about creating your own déjà vu so to speak, it was more like creating your own destiny. Visualize what you want not what you do not. At least that is what I got out of it. There are so many self-help things in this book that I never would have dreamed of. Now I didn't need the part about attaining a relationship, but that's because I already have one but there were a few things there for improving our relationship and our future. Also loved the quotes throughout the book some famous and some not so famous, but they should be. The weight loss part was really interesting and may reread that one. I look forward to trying to implement some of the things suggested in his this book and I hope you do too
Now I will leave off here. I hope you enjoy this book as much as I did. If you do like this book, please consider leaving a review. The Authors really like it when you do; they value your opinions too