This is a book on neural networks for non-technical readers.
Author: Alan French
This is a book on neural networks for non-technical readers. Nowadays, when AI and neural networks influence and control the lives of all, everyone needs to have a very basic idea of what neural networks are and how they work. This book explains neural networks in sufficient depth for a non-CS university course.
This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks.
Author: Anthony L. Caterini
This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks. This SpringerBrief is one step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural networks.This SpringerBrief is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but also to those outside of the neutral network community.
As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, ...
Author: Kevin Gurney
Publisher: CRC Press
Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.
This volume consists of chapters written by eminent scientists and engineers from the international community and present significant advances in several theories, methods and applications of an interdisciplinary research.
Author: Panos M. Pardalos
This volume consists of chapters written by eminent scientists and engineers from the international community and present significant advances in several theories, methods and applications of an interdisciplinary research. These contributions focus on both old and recent developments of Global Optimization Theory, Convex Analysis, Calculus of Variations, Discrete Mathematics and Geometry, as well as several applications to a large variety of concrete problems, including applications of computers to the study of smoothness and analyticity of functions, applications to epidemiological diffusion, networks, mathematical models of elastic and piezoelectric fields, optimal algorithms, stability of neutral type vector functional differential equations, sampling and rational interpolation for non-band-limited signals, recurrent neural network for convex optimization problems and experimental design. The book also contains some review works, which could prove particularly useful for a broader audience of readers in Mathematical and Engineering subjects and especially to graduate students who search for the latest information.
This volume of research papers comprises the proceedings of the first International Conference on Mathematics of Neural Networks and Applications (MANNA), which was held at Lady Margaret Hall, Oxford from July 3rd to 7th, 1995 and attended ...
Author: Stephen W. Ellacott
Publisher: Springer Science & Business Media
This volume of research papers comprises the proceedings of the first International Conference on Mathematics of Neural Networks and Applications (MANNA), which was held at Lady Margaret Hall, Oxford from July 3rd to 7th, 1995 and attended by 116 people. The meeting was strongly supported and, in addition to a stimulating academic programme, it featured a delightful venue, excellent food and accommo dation, a full social programme and fine weather - all of which made for a very enjoyable week. This was the first meeting with this title and it was run under the auspices of the Universities of Huddersfield and Brighton, with sponsorship from the US Air Force (European Office of Aerospace Research and Development) and the London Math ematical Society. This enabled a very interesting and wide-ranging conference pro gramme to be offered. We sincerely thank all these organisations, USAF-EOARD, LMS, and Universities of Huddersfield and Brighton for their invaluable support. The conference organisers were John Mason (Huddersfield) and Steve Ellacott (Brighton), supported by a programme committee consisting of Nigel Allinson (UMIST), Norman Biggs (London School of Economics), Chris Bishop (Aston), David Lowe (Aston), Patrick Parks (Oxford), John Taylor (King's College, Lon don) and Kevin Warwick (Reading). The local organiser from Huddersfield was Ros Hawkins, who took responsibility for much of the administration with great efficiency and energy. The Lady Margaret Hall organisation was led by their bursar, Jeanette Griffiths, who ensured that the week was very smoothly run.
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 networks.
Author: Michael Taylor
Publisher: Independently Published
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 networks. What's Inside - 'Make Your Own Neural Network: 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 Network: 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 Network: 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. Neural Network: Classify Images 10 minutes. That's all it takes to build an image classifier thanks to Google! We will provide a high-level overview of how to classify images using a convolutional neural network (CNN) and Google's Inception V3 model. Once finished, you will be able to tweak this code to classify any type of image sets! Cats, bats, super heroes - the sky's the limit.
- Tariq Rashid hat eine besondere Fähigkeit, schwierige Konzepte verständlich zu erklären, dadurch werden Neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.
Author: Tariq Rashid
Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Dennoch verstehen nur wenige, wie Neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie Neuronale Netze arbeiten. Dafür brauchen Sie keine tieferen Mathematik-Kenntnisse, denn alle mathematischen Konzepte werden behutsam und mit vielen Illustrationen erläutert. Dann geht es in die Praxis: Sie programmieren Ihr eigenes Neuronales Netz mit Python und bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. Zum Schluss lassen Sie das Netz noch auf einem Raspberry Pi Zero laufen. - Tariq Rashid hat eine besondere Fähigkeit, schwierige Konzepte verständlich zu erklären, dadurch werden Neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.
This concise, readable book provides a sampling of the very large, active, and expanding field of artificial neural network theory.
Author: Martin Anthony
This concise, readable book provides a sampling of the very large, active, and expanding field of artificial neural network theory. It considers select areas of discrete mathematics linking combinatorics and the theory of the simplest types of artificial neural networks. Neural networks have emerged as a key technology in many fields of application, and an understanding of the theories concerning what such systems can and cannot do is essential. Some classical results are presented with accessible proofs, together with some more recent perspectives, such as those obtained by considering decision lists. In addition, probabilistic models of neural network learning are discussed. Graph theory, some partially ordered set theory, computational complexity, and discrete probability are among the mathematical topics involved. Pointers to further reading and an extensive bibliography make this book a good starting point for research in discrete mathematics and neural networks.
Thorough, compact, and self-contained, this explanation and analysis of a broad range of neural nets is conveniently structured so that readers can first gain a quick global understanding of neural nets -- without the mathematics -- and can ...
Author: Leo P. J. Veelenturf
Thorough, compact, and self-contained, this explanation and analysis of a broad range of neural nets is conveniently structured so that readers can first gain a quick global understanding of neural nets -- without the mathematics -- and can then delve into mathematical specifics as necessary. The behavior of neural nets is first explained from an intuitive perspective; the formal analysis is then presented; and the practical implications of the formal analysis are stated separately. Analyzes the behavior of the six main types of neural networks -- The Binary Perceptron, The Continuous Perceptron (Multi-Layer Perceptron), The Bidirectional Memories, The Hopfield Network (Associative Neural Nets), The Self-Organizing Neural Network of Kohonen, and the new Time Sequentional Neural Network. For technically-oriented individuals working with information retrieval, pattern recognition, speech recognition, signal processing, data classification.
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.
Author: Michael Taylor
Publisher: Independently Published
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. 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.
Discover How to Build Your Own Neural Network From Scratch...Even if You've Got Zero Math or Coding Skills! What seemed like a lame and unbelievable sci-fi movie a few decades ago is now a reality. Machines can finally think. Maybe not quite as complex as the human brain, but more than enough to make everyone's life a lot easier. Artificial neural networks, based on the neurons found in the human brain give machines a 'brain'. Patterned just like biological neurons, these software or hardware are a variety of the deep learning technology. With their help you can make your computer learn by feeding it data, which will then be generated as the output you desire. It is they to thank for the nanoseconds in which computers operate. It may be science, but it is not actually rocket science. Everyone can learn how to take advantage of the progressed technology of today, get inside the 'brain' of the computers, and train them to perform the desired operations. They have been used in many different industries, and you can rest assured that you will find the perfect purpose for your own neural network. The best part about this book is that it doesn't require a college degree. Your high school math skills are quite enough for you to get a good grasp of the basics and learn how to build an artificial neural network. From non-mathematical explanations to teaching you the basic math behind the ANNs and training you how to actually program one, this book is the most helpful guide you will ever find. Carefully designed for you, the beginner, this guide will help you become a proud owner of a neural network in no time. Here's a Sneak Peak to What You'll Discover Inside this Book: The 6 unique benefits of neural networks The difference between biological and artificial neural networks And inside look into ANN (Artificial Neural Networks) The industries ANN is used in How to teach neural networks to perform specific commands The different types of learning modalities (e.g. Hebbian Learning, unsupervised learning, supervised learning etc.) The architecture of ANN Basic math behind artificial neurons Simple networks for pattern classification The Hebb Rule How to build a simple neural network code The backpropogation algorithm and how to program it And much, much more! There's a lot more inside this book we'll cover, so be prepared. I've made to lucidly explain everything I cover so that there's zero confusion! Download this book today and discover all the intricate details of building your very own Neural Network
Author: Manpreet Singh GhotraPublish On: 2017-11-10
Style and Approach This book is designed to give you just the right number of concepts to back up the examples. With real-world use cases and problems solved, this book is a handy guide for you.
Author: Manpreet Singh Ghotra
Publisher: Packt Publishing Ltd
Neural Networks and their implementation decoded with TensorFlow About This Book Develop a strong background in neural network programming from scratch, using the popular Tensorflow library. Use Tensorflow to implement different kinds of neural networks – from simple feedforward neural networks to multilayered perceptrons, CNNs, RNNs and more. A highly practical guide including real-world datasets and use-cases to simplify your understanding of neural networks and their implementation. Who This Book Is For This book is meant for developers with a statistical background who want to work with neural networks. Though we will be using TensorFlow as the underlying library for neural networks, book can be used as a generic resource to bridge the gap between the math and the implementation of deep learning. If you have some understanding of Tensorflow and Python and want to learn what happens at a level lower than the plain API syntax, this book is for you. What You Will Learn Learn Linear Algebra and mathematics behind neural network. Dive deep into Neural networks from the basic to advanced concepts like CNN, RNN Deep Belief Networks, Deep Feedforward Networks. Explore Optimization techniques for solving problems like Local minima, Global minima, Saddle points Learn through real world examples like Sentiment Analysis. Train different types of generative models and explore autoencoders. Explore TensorFlow as an example of deep learning implementation. In Detail If you're aware of the buzz surrounding the terms such as "machine learning," "artificial intelligence," or "deep learning," you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that. You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders. By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs. Style and Approach This book is designed to give you just the right number of concepts to back up the examples. With real-world use cases and problems solved, this book is a handy guide for you. Each concept is backed by a generic and real-world problem, followed by a variation, making you independent and able to solve any problem with neural networks. All of the content is demystified by a simple and straightforward approach.
If you want to learn about Neural Networks then keep reading... Aladdin from "The Arabian Nights" had a magic lamp that fulfilled his every wish when rubbed. Today we have a smartphone that serves as a window to a whole universe of knowledge, entertainment and even wise personal assistants, such as Siri - all we have to do is rub the screen. Aladdin's lamp was powered by a genie, but what powers Siri? Neural networks. It's an astounding concept that tries to mimic the way living brains work by amalgamating human and machine ways of thinking. The goal of this book is to present the reader with a digestible, readable explanation of neural networks while keeping the underlying concepts intact. The reader will acquire fundamental knowledge of neural networks through loosely related chapters that nonetheless reference terms and ideas mentioned throughout the book. The book itself isn't meant to be strictly academic, but a blend of colloquial and technical that brings this exciting, yet eerie, topic to the widest swath of the general public. There is a lot of coding and math behind neural networks, but the reader is presumed to have no prior knowledge or interest in either, so the concepts are broken down and elaborated on as such. Each chapter is made as standalone as possible to allow the reader to skip back and forth without getting lost, with the glossary at the very end serving as a handy summary. Where possible, references have been included to support the presented conclusions and encourage the reader to scrutinize the traditional media in search of clues. Neural Networks: An Essential Beginners Guide to Artificial Neural Networks and their Role in Machine Learning and Artificial Intelligence cover topics such as: Programming a smart(er) computer Composition Giving neural networks legs to stand on The magnificent wetware Personal assistants Tracking users in the real world Self-driving neural networks Taking everyone's job Quantum leap in computing Attacks on neural networks Neural network war Ghost in the machine No backlash And Much, Much More So if you want to learn about Neural Networks without having to go through heavy textbooks, click "add to cart"!
This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms.
Author: Jay Dawani
Publisher: Packt Publishing Ltd
A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures Key Features Understand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networks Learn the mathematical concepts needed to understand how deep learning models function Use deep learning for solving problems related to vision, image, text, and sequence applications Book Description Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models. You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application. By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL. What you will learn Understand the key mathematical concepts for building neural network models Discover core multivariable calculus concepts Improve the performance of deep learning models using optimization techniques Cover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizer Understand computational graphs and their importance in DL Explore the backpropagation algorithm to reduce output error Cover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs) Who this book is for This book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.
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.
Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits.
Author: Ronald T Kneusel
Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You'll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You'll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network. In addition you'll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.
Author: Alexander I. GalushkinPublish On: 2007-10-29
This book, written by a leader in neural network theory in Russia, uses mathematical methods in combination with complexity theory, nonlinear dynamics and optimization.
Author: Alexander I. Galushkin
Publisher: Springer Science & Business Media
This book, written by a leader in neural network theory in Russia, uses mathematical methods in combination with complexity theory, nonlinear dynamics and optimization. It details more than 40 years of Soviet and Russian neural network research and presents a systematized methodology of neural networks synthesis. The theory is expansive: covering not just traditional topics such as network architecture but also neural continua in function spaces as well.
This book introduces the deterministic aspects of the mathematical theory behind neural networks in a comprehensive way.
Author: Steve Ellacott
Publisher: Coriolis Group
Neural networks provide a powerful approach to problems of machine learning and pattern recognition. the underlying mathematics, however, has much more in common with classical applied mathematics. This book introduces teh deterministic aspects of the mathematical theory in a comprehensive way.
This textbook is intended for a first-year graduate course on Artificial Neural Networks.
Author: Daniel Graupe
Publisher: World Scientific
This textbook is intended for a first-year graduate course on Artificial Neural Networks. It assumes no prior background in the subject and is directed to MS students in electrical engineering, computer science and related fields, with background in at least one programming language or in a programming tool such as Matlab, and who have taken the basic undergraduate classes in systems or in signal processing.