A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.
|Make Your Own Neural Network: An In-depth Visual Introduction For Beginners|
by Michael Taylor (Author)
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 book: * A deep understanding of how a Neural Network works. * How to build a Neural Network from scratch using Python. Who this book is for: * 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...
|Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems|
by Aurélien Géron (Author)
Graphics in this book are printed in black and white.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author...
|The Math of Neural Networks|
by Michael Taylor (Author)
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...
|Deep Learning (Adaptive Computation and Machine Learning series)|
by Ian Goodfellow (Author), Yoshua Bengio (Author), Aaron Courville (Author)
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject."
―Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and...
|Deep Learning: A Practitioner's Approach|
by Josh Patterson (Author), Adam Gibson (Author)
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.
Authors Adam Gibson and Josh Patterson provide theory on deep learning before...
|Neural Network Design (2nd Edition)|
by Martin T Hagan (Author), Howard B Demuth (Author), Mark H Beale (Author), Orlando De Jesús (Author)
This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems. Features Extensive coverage of training methods for both feedforward networks (including multilayer and radial basis networks) and recurrent networks. In addition to...
|Mathematics: A Very Short Introduction|
by Timothy Gowers (Author)
The aim of this book is to explain, carefully but not technically, the differences between advanced, research-level mathematics, and the sort of mathematics we learn at school. The most fundamental differences are philosophical, and readers of this book will emerge with a clearer understanding of paradoxical-sounding concepts such as infinity, curved space, and imaginary numbers. The first few chapters are about general aspects of mathematical thought. These are followed by discussions of more...
|Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms|
by Nikhil Buduma (Author), Nicholas Locascio (Contributor)
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.
Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty...
|Bayes' Theorem Examples: A Visual Introduction For Beginners|
by Dan Morris (Author)
***** #1 Kindle Store Bestseller in Mathematics (Throughout 2016) ***** ***** #1 Kindle Store Bestseller in Education Theory (Throughout 2017) ***** If you are looking for a short beginners guide packed with visual examples, this book is for you. Bayes' Theorem Examples: A Beginners Visual Approach to Bayesian Data Analysis If you’ve recently used Google search to find something, Bayes' Theorem was used to find your search results. The same is true for those recommendations on Netflix....
|Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition|
by Sebastian Raschka (Author), Vahid Mirjalili (Author)
Key FeaturesSecond edition of the bestselling book on Machine LearningA practical approach to key frameworks in data science, machine learning, and deep learningUse the most powerful Python libraries to implement machine learning and deep learningGet to know the best practices to improve and optimize your machine learning systems and algorithmsBook Description
Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting...