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Neural Networks for Complete Beginners

Neural Networks for Complete Beginners Author Mark Smart
ISBN-10 1543268722
Release 2017-02-23
Pages 94
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This book is an exploration of an artificial neural network. It has been created to suit even the complete beginners to artificial neural networks. The first part of the book is an overview of artificial neural networks so as to help the reader understand what they are. You will also learn the relationship between the neurons which make up the human brain and the artificial neurons. Artificial neural networks embrace the concept of learning which is common in human beings. This book guides you to understand how learning takes place in artificial neural networks. The back-propagation algorithm, which is used for training artificial neural networks, is discussed. The book also guides you through the architecture of an artificial neural network. The various types of artificial neural networks based on their architecture are also discussed. The book guides you on the necessary steps for one to build a neural network. The perception, which is a type of an artificial neural network, is explored, and you will explore how to implement one programmatically. The following topics are discussed in this book: -What is a Neural Network? -Learning in Neural Networks -The Architecture of Neural Networks -Building Neural Networks -The Perceptron



Make Your Own Neural Network an In Depth Visual Introduction for Beginners

Make Your Own Neural Network  an In Depth Visual Introduction for Beginners Author Michael Taylor
ISBN-10 1549869132
Release 2017-10-04
Pages 248
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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.



Neural Networks and Deep Learning

Neural Networks and Deep Learning Author Pat Nakamoto
ISBN-10 1981614060
Release 2017-12-05
Pages 130
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Ready to crank up a neural network to get your self-driving car pick up the kids from school? Want to add 'Deep Learning' to your LinkedIn profile? Well, hold on there... Before you embark on your epic journey into the world of deep learning, there is basic theory to march through first! Take a step-by-step journey through the basics of Neural Networks and Deep Learning, made so simple that...even your granny could understand it! What you will gain from this book: * A deep understanding of how a Neural Network and Deep Learning work * A basics comprehension on how to build a Deep Neural Network from scratch Who this book is for: * Beginners who want to approach the topic, but are too afraid of complex math to start! What's Inside? * A brief introduction to Machine Learning * Two main Types of Machine Learning Algorithms * A practical example of Unsupervised Learning * What are Neural Networks? * McCulloch-Pitts's Neuron * Types of activation function * Types of network architectures * Learning processes * Advantages and disadvantages * Let us give a memory to our Neural Network * The example of book writing Software * Deep learning: the ability of learning to learn * How does Deep Learning work? * Main architectures and algorithms * Main types of DNN * Available Frameworks and libraries * Convolutional Neural Networks * Tunnel Vision * Convolution * The right Architecture for a Neural Network * Test your Neural Network Hit download. Now!



Neural Network Programming with Java

Neural Network Programming with Java Author Fabio M. Soares
ISBN-10 9781787122970
Release 2017-03-14
Pages 270
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Create and unleash the power of neural networks by implementing professional Java code About This Book Learn to build amazing projects using neural networks including forecasting the weather and pattern recognition Explore the Java multi-platform feature to run your personal neural networks everywhere This step-by-step guide will help you solve real-world problems and links neural network theory to their application Who This Book Is For This book is for Java developers who want to know how to develop smarter applications using the power of neural networks. Those who deal with a lot of complex data and want to use it efficiently in their day-to-day apps will find this book quite useful. Some basic experience with statistical computations is expected. What You Will Learn Develop an understanding of neural networks and how they can be fitted Explore the learning process of neural networks Build neural network applications with Java using hands-on examples Discover the power of neural network's unsupervised learning process to extract the intrinsic knowledge hidden behind the data Apply the code generated in practical examples, including weather forecasting and pattern recognition Understand how to make the best choice of learning parameters to ensure you have a more effective application Select and split data sets into training, test, and validation, and explore validation strategies In Detail Want to discover the current state-of-art in the field of neural networks that will let you understand and design new strategies to apply to more complex problems? This book takes you on a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java, giving you everything you need to stand out. You will first learn the basics of neural networks and their process of learning. We then focus on what Perceptrons are and their features. Next, you will implement self-organizing maps using practical examples. Further on, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning, and characters recognition (OCR). Finally, you will learn methods to optimize and adapt neural networks in real time. All the examples generated in the book are provided in the form of illustrative source code, which merges object-oriented programming (OOP) concepts and neural network features to enhance your learning experience. Style and approach This book takes you on a steady learning curve, teaching you the important concepts while being rich in examples. You'll be able to relate to the examples in the book while implementing neural networks in your day-to-day applications.



An Introduction to Neural Networks

An Introduction to Neural Networks Author Kevin Gurney
ISBN-10 9781482286991
Release 2014-04-21
Pages 234
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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.



Introduction to Neural Networks with Java

Introduction to Neural Networks with Java Author Jeff Heaton
ISBN-10 9781604390087
Release 2008
Pages 440
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Introduction to Neural Networks in Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures such as the feedforward, Hopfield, and Self Organizing Map networks are discussed. Training techniques such as Backpropagation, Genetic Algorithms and Simulated Annealing are also introduced. Practical examples are given for each neural network. Examples include the Traveling Salesman problem, handwriting recognition, financial prediction, game strategy, learning mathematical functions and special application to Internet bots. All Java source code can be downloaded online.



Machine Learning for Absolute Beginners

Machine Learning for Absolute Beginners Author Oliver Theobald
ISBN-10 1549617214
Release 2018
Pages 160
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"The manner in which computers are now able to mimic human thinking to process information is rapidly exceeding human capabilities in everything from chess to picking the winner of a song contest. In the modern age of machine learning, computers do not strictly need to receive an 'input command' to perform a task, but rather 'input data'. From the input of data they are able to form their own decisions and take actions virtually as a human world. But given it is a machine, it can consider many more scenarios and execute far more complicated calculations to solve complex problems. This is the element that excites data scientists and machine learning engineers the most. The ability to solve complex problems never before attempted. This book will dive in to introduce machine learning, and is ideal for beginners starting out in machine learning."--page 4 of cover.



Neural Network Programming With Python

Neural Network Programming With Python Author Max Sharp
ISBN-10 1539381951
Release 2016-10-18
Pages
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This book is a guide on how to implement a neural network in the Python programming language. It begins by giving you a brief overview of neural networks so as to know what they are, where they are used, and how they are implemented. The next step is an exploration of the backpropagation algorithm. This is the algorithm behind the functionality of neural networks, and it involves a forward and backward pass. Numby is a Python library which can be used for the purpose of implementation of a neural network. This library is discussed in this book, and you are guided on how to use it for that purpose. The functionality of neural networks has to be improved. The various ways to improve how a neural network works is also explored. You are then guided on how to implement neural networks with Neupy, another Python library. The following topics are discussed in this book: - A Brief Overview of Neural Networks - Backpropagation Algorithm - Neural Networks with Numpy - Improving a Neural Network in Python - Neupy - Models in Neural Networks



Make Your Own Neural Network

Make Your Own Neural Network Author Tariq Rashid
ISBN-10 1530826608
Release 2016-03-31
Pages 222
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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.



Artificial Neural Networks

Artificial Neural Networks Author Kevin L. Priddy
ISBN-10 0819459879
Release 2005-01-01
Pages 165
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This tutorial text provides the reader with an understanding of artificial neural networks (ANNs) and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks.



Introduction to Neural Networks Using Matlab 6 0

Introduction to Neural Networks Using Matlab 6 0 Author S. N. Sivanandam
ISBN-10 0070591121
Release 2006
Pages 656
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Introduction to Neural Networks Using Matlab 6 0 has been writing in one form or another for most of life. You can find so many inspiration from Introduction to Neural Networks Using Matlab 6 0 also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Introduction to Neural Networks Using Matlab 6 0 book for free.



Convolutional Neural Networks in Python

Convolutional Neural Networks in Python Author Anthony Williams
ISBN-10 1973908786
Release 2017-07-25
Pages 106
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Convolutional Neural Networks in Python (2nd Edition) Deep learning has been a great part of various scientific fields and since this is my third book regarding this topic, you already know the great significance of deep learning in comparison to traditional methods. At this point, you are also familiar with types of neural networks and their wide range of applications including image and speech recognition, natural language processing, video game development and other. On the other hand, this book is all about convolutional neural networks and how to use these neural networks in various tasks of automatic image and speech recognition in Python. You will also get a better insight into the architecture of convolutional layers as we are going deeper into this subject. Deep learning is pretty complex subject, but since you already have a fundamental knowledge of this topic, getting to know convolutional neural networks better is next logical step. What you will learn in Convolutional Neural Networks in Python: Architecture of convolutional neural networks Solving computer vision tasks using convolutional neural networks Python and computer vision Automatic image and speech recognition Theano and TenroeFlow image recognition How to use MNIST vision dataset What are commonly used convolutional filters Get this book today and learn more about Convolutional Neural Networks in Python!! PS: Get the Paperback and get this Ebook for FREE!!



Fundamentals of Deep Learning

Fundamentals of Deep Learning Author Nikhil Buduma
ISBN-10 9781491925560
Release 2017-05-25
Pages 298
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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 complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Learn the fundamentals of reinforcement learning



Neural Networks

Neural Networks Author Simon Haykin
ISBN-10 0780334949
Release 1999-01
Pages 700
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Neural Networks has been writing in one form or another for most of life. You can find so many inspiration from Neural Networks also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Neural Networks book for free.



An Introduction to Neural Networks

An Introduction to Neural Networks Author James A. Anderson
ISBN-10 0262510812
Release 1995
Pages 650
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An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author's own ideas. Students learn how to teach arithmetic to a neural network and get a short course on linear associative memory and adaptive maps. They are introduced to the author's brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject. The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. As the gap between these two groups widens, Anderson notes that the academics have tended to drift off into irrelevant, often excessively abstract research while the engineers have lost contact with the source of ideas in the field. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.



Deep Learning and Artificial Intelligence

Deep Learning and Artificial Intelligence Author John Slavio
ISBN-10 1977610722
Release 2017-09-24
Pages 76
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Are you looking to get started in the world of Deep Learning, Artificial Intelligence and Neural Networks? Then Read Below. Welcome to this book on Deep Learning and Neural Networks. We're going to be diving into what neural networks are, what the current neural networks out there do, with an API. Once we go over how everything works and how each of these new technologies work, we will also go over the many different applications that can be applied to general life and business due to the creation of neural networks. Now I want you to realize that neural networks are not a complicated topic but it may feel like a complicated topic. There have been a lot of news stories about how there are going to be self-driving cars, machines that make their own products, and many other different applications of neural networks that make it sound like a vastly complicated machine. However, the tool of the neural network is a very simple tool. When you hear about the applications that are being created that utilize neural networks, you are actually hearing about the amount of work that went behind making a neural network do something that's complicated but not a complicated neural network. Neural networks are extremely easy to understand as you will find throughout this book but the problem is that people have made them look complicated. Therefore, let's go ahead and demystify this subject so that you can get into the field of neural networks yourself and have some fun. Here's What's Included In This Book: What are Neural Networks? Biological Neural Networks Artificial Neural Networks Keras Model and Layers Different Deep Learning Algorithms Benefits of Neural Networks Business Applications of Neural Networks Scroll up and Download.



Introduction to Neural Networks

Introduction to Neural Networks Author
ISBN-10 9781349135301
Release 1994-11-11
Pages 168
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Introduction to Neural Networks has been writing in one form or another for most of life. You can find so many inspiration from Introduction to Neural Networks also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Introduction to Neural Networks book for free.