make your own neural network

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Make Your Own Neural Network

Author : Tariq Rashid
ISBN : 1530826608
Genre :
File Size : 70. 29 MB
Format : PDF, Docs
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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.

Build Your Own Neural Network Today

Author : N. D. Lewis
ISBN : 1519101236
Genre :
File Size : 65. 89 MB
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BUILD YOUR OWN NEURAL NETWORK TODAY! With an EASY to follow process showing you how to build them FASTER than you imagined possible using R About This Book This rich, fascinating, accessible hands on guide, puts neural networks firmly into the hands of the practitioner. It reveals how they work, and takes you under the hood with an easy to follow process showing you how to build them faster than you imagined possible using the powerful, free R predictive analytics package. Everything you need to get started is contained within this book. It is your detailed, practical, tactical hands on guide. To accelerate your success, it contains exercises with fully worked solutions also provided. Once you have mastered the process, it will be easy for you to translate your knowledge into other powerful applications. A book for everyone interested in machine learning, predictive analytics, neural networks and decision science. Here is what it can do for you: SAVE TIME: Imagine having at your fingertips easy access to the very best neural network models without getting bogged down in mathematical details. In this book, you'll learn fast effective ways to build powerful neural network models easily using R. LEARN EASILY: Build Your Own Neural Network TODAY! Contains an easy to follow process showing you how to build the most successful neural networks used for learning from data; use this guide and build them easily and quickly. BOOST PRODUCTIVITY: Bestselling author and data scientist Dr. N.D. Lewis will show you how to build neural network models in less time than you ever imagined possible! Even if you're a busy professional, a student or hobbyist with little time, you will rapidly enhance your knowledge. EFFORTLESS SUCCESS: By spending as little as 10 minutes a day working through the dozens of real world examples, illustrations, practitioner tips and notes, you'll be able to make giant leaps forward in your knowledge, broaden your skill-set and generate new ideas for your own personal use. ELIMINATE ANXIETY: Forget trying to master every single mathematical detail, instead your goal is to simply to follow the process using real data that only takes about 5 to 15 minutes to complete. Within this process is a series of actions by which the neural network model is explained and constructed. All you have to do is follow the process. It is your checklist for use and reuse. 1 For people interested in statistics, machine learning, data analysis, data mining, and future hands-on practitioners seeking a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. Here are some of the neural network models you will build: Multi layer Perceptrons Probabilistic Neural Networks Generalized Regression Neural Networks Recurrent Neural Networks Buy the book today. Your next big breakthrough using neural networks is only a page away!

Introduction To Neural Networks With Java

Author : Jeff Heaton
ISBN : 9781604390087
Genre : Computers
File Size : 47. 46 MB
Format : PDF, Mobi
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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.

An Introduction To Neural Networks

Author : Kevin Gurney
ISBN : 9780203451519
Genre : Computers
File Size : 78. 70 MB
Format : PDF
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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.

Neural Network Design 2nd Edition

Author : Martin Hagan
ISBN : 0971732116
Genre :
File Size : 43. 78 MB
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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.

Building Neural Networks

Author : David M. Skapura
ISBN : 0201539217
Genre : Computers
File Size : 33. 65 MB
Format : PDF
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This practical introduction describes the kinds of real-world problems neural network technology can solve. Surveying a range of neural network applications, the book demonstrates the construction and operation of artificial neural systems. Through numerous examples, the author explains the process of building neural-network applications that utilize recent connectionist developments, and conveys an understanding both of the potential, and the limitations of different network models. Examples are described in enough detail for you to assimilate the information and then use the accumulated experience of others to create your own applications. These examples are deliberately restricted to those that can be easily understood, and recreated, by any reader, even the novice practitioner. In some cases the author describes alternative approaches to the same application, to allow you to compare and contrast their advantages and disadvantages. Organized by application areas, rather than by specific network architectures or learning algorithms, Builiding Neural Networks shows why certain networks are more suitable than others for solving specific kinds of problems. Skapura also reviews principles of neural information processing and furnishes an operations summary of the most popular neural-network processing models. Finally, the book provides information on the practical aspects of application design, and contains six topic-oriented chapters on specific applications of neural-network systems. These applications include networks that perform: Pattern matching, storage, and recall Business and financial systems Data extraction from images Mechanical process control systems New neural networks that combine pattern matching with fuzzy logic The book includes application-oriented excercises that further help you see how a neural network solves a problem, and that reinforce your understanding of modeling techniques. 0201539217B04062001

Python Machine Learning

Author : Sebastian Raschka
ISBN : 9781783555147
Genre : Computers
File Size : 21. 9 MB
Format : PDF, ePub, Mobi
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Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

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