*Introduction for Neural Network Programming*

**Author**: Mark Smart

**Publisher:**Createspace Independent Publishing Platform

**ISBN:**9781543268720

**Category:**

**Page:**94

**View:**1184

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# Search Results for: neural-networks-for-complete-beginners-introduction-for-neural-network-programming

*Introduction for Neural Network Programming*

**Author**: Mark Smart

**Publisher:** Createspace Independent Publishing Platform

**ISBN:** 9781543268720

**Category:**

**Page:** 94

**View:** 1184

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

**Author**: Kevin Gurney

**Publisher:** CRC Press

**ISBN:** 1482286998

**Category:** Computers

**Page:** 234

**View:** 6791

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 & Tricks*

**Author**: Marcelo Bosque

**Publisher:** iUniverse

**ISBN:** 0595219969

**Category:** Computers

**Page:** 148

**View:** 3920

There is a deep desire in men, in order to reproduce intelligence and place it in a machine. Neural Networks are an attempt to reproduce the synaptic connections of our brain in a computer. Duplicating the way we use our neurons to think in a machine, it is expected to have a device that could be able to do "intelligent" tasks, the ones reserved just to humans some time ago. Neural Network are a reality now, not a fantasy, and they have been made in order to recognize patterns (a face ,a photograph or a song, are patterns) and forecast trends. I have seen many books about this subject in my life. All of them are hard to read, and tedious to learn, so I decided to make my own one. For beginner readers, I have tried to use a simple language, in order to be understood by anyone who wants to know about nets. An easy to read, practical and concise work. If you are interested in the brain functions and how can we simulate it in a computer, you'll get here a different way to penetrate into their secrets.For advanced readers who want to make their own nets, I have included a methodology for building neural networks and complete sample computer source-code with tricks that will save you a lot of time while designing it.
*Deep Learning Explained to Your Granny - a Visual Introduction for Beginners Who Want to Make Their Own Deep Learning Neural Network*

**Author**: Pat Nakamoto

**Publisher:** Createspace Independent Publishing Platform

**ISBN:** 9781981614066

**Category:**

**Page:** 130

**View:** 8001

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!
*Concepts, Tools and Techniques Explained for Absolute Beginners*

**Author**: François Duval

**Publisher:** Createspace Independent Publishing Platform

**ISBN:** 9781985134560

**Category:**

**Page:** 128

**View:** 2733

***** Buy now (Will soon return to $75.99 + Special Offer Below) ***** Free Kindle eBook for customers who purchase the print book from Amazon Are you thinking of learning more about Artificial Neural Network? This book has been written in layman's terms as an introduction to neural networks and their algorithms. Each algorithm is explained very easily for more understanding. Several Visual Illustrations and Examples Instead of tough math formulas, this book contains several graphs and images which detail all algorithms and their applications in all area of the real life. Why this book is different ? An Artificial Neural Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human (animal) brain processes information. It includes a large number of connected processing units called neurons that work together to process information. They also generate meaningful results from it. In this book, we will take you through the complete introduction to Artificial Neural Network, Artificial Neural Network Structure, layers of ANN, Applications, Algorithms, Tools and technology, Practical implementations and the benefits and limitations of ANN. This book takes a different approach that is based on providing simple examples of how ANN algorithms work, and building on those examples step by step to encompass the more complicated parts of the algorithms. Target Users The book designed for a variety of target audiences. The most suitable users would include: Beginners who want to approach ANN, but are too afraid of complex math to start Newbies in computer science techniques and ANN Professionals in data science and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on neural networks and deep learning What's inside this book? What is Artificial Neural Network? Why Neural Networks? Major Variants of Artificial Neural Network Tools and Technologies Practical implementations Major NN projects Open sources resources Issues and Challenges Applications of ANN Deep Learning: What & Why? Our Future with Deep Learning Applied The Long-Term Vision of Deep Learning Glossary of Some Useful Terms in Neural Networks Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: If you want to learn more about deep learning with practical applications, this book is for you. This book has been written in layman's terms as an introduction to neural networks and their algorithms. Each algorithm is explained very easily for more understanding. No coding experience is required. Some practical examples is presented with Python but it is not the major part of the book. Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a Neural Networks expert? A: Unfortunately, no. This book is designed for readers taking their first steps in neural networks and further learning will be required beyond this book to master all aspects of neural networks. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. will also be happy to help you if you send us an email at [email protected]

**Author**: Fabio M. Soares,Alan M. F. Souza

**Publisher:** Packt Publishing Ltd

**ISBN:** 1787122972

**Category:** Computers

**Page:** 270

**View:** 6132

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.

**Author**: Jeff Heaton

**Publisher:** Heaton Research, Inc.

**ISBN:** 1604390085

**Category:** Computers

**Page:** 440

**View:** 8894

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.
*A Comprehensive Foundation*

**Author**: Simon Haykin

**Publisher:** IEEE

**ISBN:** 9780780334946

**Category:** Computers

**Page:** 700

**View:** 8550

*Designing Next-Generation Machine Intelligence Algorithms*

**Author**: Nikhil Buduma,Nicholas Locascio

**Publisher:** "O'Reilly Media, Inc."

**ISBN:** 1491925566

**Category:** Computers

**Page:** 298

**View:** 1373

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

**Author**: Tariq Rashid

**Publisher:** Createspace Independent Publishing Platform

**ISBN:** 9781530826605

**Category:**

**Page:** 222

**View:** 5701

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.

**Author**: James A. Anderson

**Publisher:** MIT Press

**ISBN:** 9780262510813

**Category:** Computers

**Page:** 650

**View:** 4123

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.
*Create Your Own Neural Network!*

**Author**: Max Sharp

**Publisher:** N.A

**ISBN:** 9781539381952

**Category:**

**Page:** N.A

**View:** 2916

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
*Unleash the power of TensorFlow to train efficient neural networks*

**Author**: Manpreet Singh Ghotra,Rajdeep Dua

**Publisher:** Packt Publishing Ltd

**ISBN:** 1788397754

**Category:** Computers

**Page:** 274

**View:** 2808

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.

**Author**: N.A

**Publisher:** Macmillan International Higher Education

**ISBN:** 1349135305

**Category:**

**Page:** 168

**View:** 2397

**Author**: S. N. Sivanandam,S. N Deepa

**Publisher:** Tata McGraw-Hill Education

**ISBN:** 9780070591127

**Category:** MATLAB.

**Page:** 656

**View:** 1654

**Author**: Martin Hagan,Howard Demuth,Mark Beale,Orlando De Jesus

**Publisher:** N.A

**ISBN:** 9780971732117

**Category:**

**Page:** 800

**View:** 6276

This book 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.
*A Systematic Introduction*

**Author**: Raul Rojas

**Publisher:** Springer Science & Business Media

**ISBN:** 3642610684

**Category:** Computers

**Page:** 502

**View:** 7017

Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.

**Author**: Ian Goodfellow,Yoshua Bengio,Aaron Courville

**Publisher:** MIT Press

**ISBN:** 0262337371

**Category:** Computers

**Page:** 800

**View:** 1718

"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 understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

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