Neural Networks Computers With Intuition
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Neural Networks
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Author : S?ren Brunak
language : en
Publisher: World Scientific
Release Date : 1990
Neural Networks written by S?ren Brunak and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Computers categories.
Both specialists and laymen will enjoy reading this book. Using a lively, non-technical style and images from everyday life, the authors present the basic principles behind computing and computers. The focus is on those aspects of computation that concern networks of numerous small computational units, whether biological neural networks or artificial electronic devices.
Neural Networks Computers With Intuition
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Author : Benny Elley Lautrup
language : en
Publisher: World Scientific
Release Date : 1990-05-01
Neural Networks Computers With Intuition written by Benny Elley Lautrup and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990-05-01 with Science categories.
Both specialists and laymen will enjoy reading this book. Using a lively, non-technical style and images from everyday life, the authors present the basic principles behind computing and computers. The focus is on those aspects of computation that concern networks of numerous small computational units, whether biological neural networks or artificial electronic devices.
Neural Networks For Time Series Forecasting With R
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Author : N. Lewis
language : en
Publisher:
Release Date : 2017-03-27
Neural Networks For Time Series Forecasting With R written by N. Lewis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-27 with categories.
Finally, A Blueprint for Neural Network Time Series Forecasting with R! Neural Networks for Time Series Forecasting with R offers a practical tutorial that uses hands-on examples to step through real-world applications using clear and practical case studies. Through this process it takes you on a gentle, fun and unhurried journey to creating neural network models for time series forecasting with R. Whether you are new to data science or a veteran, this book offers a powerful set of tools for quickly and easily gaining insight from your data using R. NO EXPERIENCE REQUIRED: This book uses plain language rather than a ton of equations; I'm assuming you never did like linear algebra, don't want to see things derived, dislike complicated computer code, and you're here because you want to try neural networks for time series forecasting for yourself. YOUR PERSONAL BLUE PRINT: Through a simple to follow step by step process, you will learn how to build neural network time series forecasting models using R. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful applications. THIS BOOK IS FOR YOU IF YOU WANT: Explanations rather than mathematical derivation Practical illustrations that use real data. Illustrations to deepen your understanding. Worked examples in R you can easily follow and immediately implement. Ideas you can actually use and try on your own data. TAKE THE SHORTCUT: This guide was written for people just like you. Individuals who want to get up to speed as quickly as possible. In this book you will learn how to: YOU'LL LEARN HOW TO: Unleash the power of Long Short-Term Memory Neural Networks. Develop hands on skills using the Gated Recurrent Unit Neural Network. Design successful applications with Recurrent Neural Networks. Deploy Jordan and Elman Partially Recurrent Neural Networks. Adapt Deep Neural Networks for Time Series Forecasting. Master the General Method of Data Handling Type Neural Networks. For each neural network model, every step in the process is detailed, from preparing the data for analysis, to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks. Using plain language, this book offers a simple, intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available using R. Everything you need to get started is contained within this book. Neural Networks for Time Series Forecasting with R is your very own hands on practical, tactical, easy to follow guide to mastery. Buy this book today and accelerate your progress!
Artificial Intuition
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Author : Carlos Perez
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-01-15
Artificial Intuition written by Carlos Perez and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-15 with categories.
I challenge you to find a field as interesting and exciting as Deep Learning. This book is a spin-off from my previous book "The Deep Learning AI Playbook." The Playbook was meant for a professional audience. This is targeted to a much wider audience. There are two kinds of audiences, those looking to explore and those looking to optimize. There are two ways to learn, learning by exploration and learning by exploitation. This book is about exploration into the emerging field of Deep Learning. It's more like a popular science book and less of a business book. It's not going to provide any practical advice of how to use or deploy Deep Learning. However, it's a book that will explore this new field in many more perspectives. So at the very least, you'll walk away with the ability to hold a very informative and impressive conversation about this unique subject. It's my hope that having less constraints on what I can express can lead to a more insightful and novel book. There are plenty of ideas that are either too general or too speculative to fit within a business oriented book. With a business book, you always want to manage expectations. Artificial Intelligence is one of those topics that you want to keep speaking in a conservative manner. That's one reason I felt the need for this book. Perhaps the freedom to be more liberal can give readers more ideas as where this field is heading. Also, it's not just business that needs to understand Deep Learning. We are all going to be profoundly impacted by this new kind of Artificial Intelligence and it is critical we all develop at least a good intuition of how it will change the world.The images in the front cover are all generated using Deep Learning technology.
An Intuitive Exploration Of Artificial Intelligence
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Author : Simant Dube
language : en
Publisher: Springer Nature
Release Date : 2021-06-21
An Intuitive Exploration Of Artificial Intelligence written by Simant Dube and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-21 with Computers categories.
This book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future. An Intuitive Exploration of Artificial Intelligence offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense potential. The book is intended to serve as a textbook for graduate and senior-level undergraduate courses in AI. Moreover, since the book provides a strong geometrical intuition about advanced mathematical foundations of AI, practitioners and researchers will equally benefit from the book.
Neural Logic Networks A New Class Of Neural Networks
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Author : Hoon Heng Teh
language : en
Publisher: World Scientific
Release Date : 1995-10-25
Neural Logic Networks A New Class Of Neural Networks written by Hoon Heng Teh and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-10-25 with Computers categories.
This book is the first of a series of technical reports of a key research project of the Real-World Computing Program supported by the MITI of Japan.The main goal of the project is to model human intelligence by a special class of mathematical systems called neural logic networks.The book consists of three parts. Part 1 describes the general theory of neural logic networks and their potential applications. Part 2 discusses a new logic called Neural Logic which attempts to emulate more closely the logical thinking process of human. Part 3 studies the special features of neural logic networks which resemble the human intuition process.This book should appeal to researchers in artificial intelligence, neural computings and logic, as well as graduate and advance undergraduate students in computer science.
Neural Networks
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Author : Raul Rojas
language : en
Publisher:
Release Date : 2014-01-15
Neural Networks written by Raul Rojas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-15 with categories.
Deep Neural Networks In A Mathematical Framework
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Author : Anthony L. Caterini
language : en
Publisher: Springer
Release Date : 2018-03-22
Deep Neural Networks In A Mathematical Framework written by Anthony L. Caterini and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-22 with Computers categories.
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.
An Introduction To Neural Network Methods For Differential Equations
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Author : Neha Yadav
language : en
Publisher: Springer
Release Date : 2015-02-26
An Introduction To Neural Network Methods For Differential Equations written by Neha Yadav and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-26 with Mathematics categories.
This book introduces a variety of neural network methods for solving differential equations arising in science and engineering. The emphasis is placed on a deep understanding of the neural network techniques, which has been presented in a mostly heuristic and intuitive manner. This approach will enable the reader to understand the working, efficiency and shortcomings of each neural network technique for solving differential equations. The objective of this book is to provide the reader with a sound understanding of the foundations of neural networks and a comprehensive introduction to neural network methods for solving differential equations together with recent developments in the techniques and their applications. The book comprises four major sections. Section I consists of a brief overview of differential equations and the relevant physical problems arising in science and engineering. Section II illustrates the history of neural networks starting from their beginnings in the 1940s through to the renewed interest of the 1980s. A general introduction to neural networks and learning technologies is presented in Section III. This section also includes the description of the multilayer perceptron and its learning methods. In Section IV, the different neural network methods for solving differential equations are introduced, including discussion of the most recent developments in the field. Advanced students and researchers in mathematics, computer science and various disciplines in science and engineering will find this book a valuable reference source.
Models Metaphors And Intuition
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Author : Michael Ruhl Frank
language : en
Publisher: Eagle Harbour Publishing
Release Date : 2023-11-28
Models Metaphors And Intuition written by Michael Ruhl Frank and has been published by Eagle Harbour Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-28 with Computers categories.
My goal in this writing is to promote social consciousness and increase awareness and understanding of the “human condition” that we all share, and that ultimately binds us all in our future, and our fate. I endeavor to pursue that goal with a series of discussions on how we think, how we learn, and how we communicate against the backdrop of our own individual consciousness, and to do so in an accessible manner. These discussions will leverage similarities of the human brain to neural networks in computing and artificial intelligence – as it has become increasingly important recently to understand these concepts.