Neural Networks

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Artificial Neural Networks The Brain Behind Ai

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Author : Jayesh Ahire
language : en
Publisher: Lulu.com
Release Date : 2018-03-24

Artificial Neural Networks The Brain Behind Ai written by Jayesh Ahire and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-24 with Computers categories.

Neural networks are one of the most popular and powerful classes of machine learning algorithms. In quantitative finance neural networks are often used for time-series forecasting, constructing proprietary indicators, algorithmic trading, securities classification and credit risk modeling. They have also been used to construct stochastic process models and price derivatives. Despite their usefulness, neural networks tend to have a bad reputation because their performance is "temperamental". In my opinion, this can be attributed to poor network design owing to misconceptions regarding how neural networks work. This book discusses every aspect of the artificial neural network in very interactive, practical and simple way.

Neural Networks For Beginners

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Author : Russel R Russo
language : en
Publisher:
Release Date : 2019-11-06

Neural Networks For Beginners written by Russel R Russo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-06 with categories.

Do you want to understand Neural Networks and learn everything about them but it looks like it is an exclusive club? Are you fascinated by Artificial Intelligence but you think that it would be too difficult for you to learn? If you think that Neural Networks and Artificial Intelligence are the present and, even more, the future of technology, and you want to be part of it... well you are in the right place, and you are looking at the right book. If you are reading these lines you have probably already noticed this: Artificial Intelligence is all around you. Your smartphone that suggests you the next word you want to type, your Netflix account that recommends you the series you may like or Spotify's personalised playlists. This is how machines are learning from you in everyday life. And these examples are only the surface of this technological revolution. Either if you want to start your own AI entreprise, to empower your business or to work in the greatest and most innovative companies, Artificial Intelligence is the future, and Neural Networks programming is the skill you want to have. The good news is that there is no exclusive club, you can easily (if you commit, of course) learn how to program and use neural networks, and to do that Neural Networks for Beginners is the perfect way. In this book you will learn: The types and components of neural networks The smartest way to approach neural network programming Why Algorithms are your friends The "three Vs" of Big Data (plus two new Vs) How machine learning will help you making predictions The three most common problems with Neural Networks and how to overcome them Even if you don't know anything about programming, Neural Networks is the perfect place to start now. Still, if you already know about programming but not about how to do it in Artificial Intelligence, neural networks are the next thing you want to learn. And Neural Networks for Beginners is the best way to do it. Download Neural Network for Beginners now to get the best start for your journey to Artificial Intelligence. Scroll to the top of the page and click the BUY NOW button.

Hands On Neural Networks

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Author : Leonardo De Marchi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-30

Hands On Neural Networks written by Leonardo De Marchi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-30 with Computers categories.

Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras Key FeaturesExplore neural network architecture and understand how it functionsLearn algorithms to solve common problems using back propagation and perceptronsUnderstand how to apply neural networks to applications with the help of useful illustrationsBook Description Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics. Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data and the role of long short-term memory networks (LSTMs) in helping you solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and reinforcement learning. Finally, you can look forward to further content on the latest advancements in the field of neural networks. By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions. What you will learnLearn how to train a network by using backpropagationDiscover how to load and transform images for use in neural networksStudy how neural networks can be applied to a varied set of applicationsSolve common challenges faced in neural network developmentUnderstand the transfer learning concept to solve tasks using Keras and Visual Geometry Group (VGG) networkGet up to speed with advanced and complex deep learning concepts like LSTMs and NLP Explore innovative algorithms like GANs and deep reinforcement learningWho this book is for If you are interested in artificial intelligence and deep learning and want to further your skills, then this intermediate-level book is for you. Some knowledge of statistics will help you get the most out of this book.

Neural Networks

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Author : Richard J. Mammone
language : en
Publisher:
Release Date : 1991

Neural Networks written by Richard J. Mammone and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Computers categories.

Neural networks have attracted the interest of scientists from many disciplines: engineering, computer science, mathematics, physics, biology, and cognitive science. This volume collects 15 contributions written by leading international researchers that illustrate important features of various neural network methodologies. Topics discussed include the fundamental principles of neural networks and various modifications of basic neural systems that improve system performance in specific application domains. Where appropriate, improvements are demonstrated by numerical examples.

Artificial Neural Networks

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Author : Robert J. Schalkoff
language : en
Publisher: McGraw-Hill Science, Engineering & Mathematics
Release Date : 1997

Artificial Neural Networks written by Robert J. Schalkoff and has been published by McGraw-Hill Science, Engineering & Mathematics this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Computers categories.

While the primary objective of the text is to provide a teaching tool, practicing engineers and scientists are likely to find the clear, concept-based treatment useful in updating their backgrounds.

Neural Networks

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Author : Raul Rojas
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29

Neural Networks written by Raul Rojas and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Computers categories.

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.

Computational Mechanics With Neural Networks

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Author : Genki Yagawa
language : en
Publisher: Springer Nature
Release Date : 2021-02-26

Computational Mechanics With Neural Networks written by Genki Yagawa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-26 with Technology & Engineering categories.

This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other machine learning method in computational mechanics. Part II highlights the applications of neural networks to a variety of problems of computational mechanics. The final chapter gives perspectives to the applications of the deep learning to computational mechanics.

Neural Networks

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Author : Simon S. Haykin
language : en
Publisher: Upper Saddle River, N.J. : Prentice Hall
Release Date : 1999

Neural Networks written by Simon S. Haykin and has been published by Upper Saddle River, N.J. : Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Computers categories.

Using a wealth of case studies to illustrate the real-life, practical applications of neural networks, this state-of-the-art text exposes students to many facets of Neural Networks.

Introduction To Deep Learning And Neural Networks With Pythontm

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Author : Ahmed Fawzy Gad
language : en
Publisher: Academic Press
Release Date : 2020-11-25

Introduction To Deep Learning And Neural Networks With Pythontm written by Ahmed Fawzy Gad and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Medical categories.

Introduction to Deep Learning and Neural Networks with PythonTM: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonTM code examples to clarify neural network calculations, by book's end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonTM examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. - Examines the practical side of deep learning and neural networks - Provides a problem-based approach to building artificial neural networks using real data - Describes PythonTM functions and features for neuroscientists - Uses a careful tutorial approach to describe implementation of neural networks in PythonTM - Features math and code examples (via companion website) with helpful instructions for easy implementation

Neural Networks

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Author : Berndt Müller
language : en
Publisher: Springer Science & Business Media
Release Date : 1995-10-02

Neural Networks written by Berndt Müller and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-10-02 with Computers categories.

Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.