Neural Networks Explained
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Neural Networks Explained
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Author : Kai Turing
language : en
Publisher: Publifye AS
Release Date : 2025-01-06
Neural Networks Explained written by Kai Turing and has been published by Publifye AS this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-06 with Computers categories.
""Neural Networks Explained"" offers a comprehensive yet accessible exploration of artificial intelligence's fundamental building blocks, making complex concepts approachable for readers without technical expertise. The book uniquely bridges the gap between advanced AI technology and everyday understanding by drawing compelling parallels between biological brains and artificial neural networks, helping readers grasp how these systems learn and make decisions. The journey begins with core concepts of neural networks, including neurons, layers, and connections, before progressing through their historical evolution and modern applications. Rather than relying on complex mathematical formulas, the book employs vivid analogies and real-world examples, such as how neural networks power smartphone facial recognition or distinguish between images of cats and dogs. This practical approach makes technical concepts tangible for business professionals, students, and curious individuals alike. Through a combination of case studies, expert interviews, and documented examples, the book examines neural networks' impact across various industries, from healthcare diagnostics to autonomous vehicles. It thoughtfully addresses contemporary debates surrounding AI ethics and bias while maintaining scientific accuracy. The interdisciplinary perspective, connecting computer science with neuroscience and psychology, provides readers with a holistic understanding of both the technology's capabilities and its broader implications for society, making it an invaluable resource for anyone seeking to navigate our increasingly AI-driven world.
Explaining Neural Networks In Raw Python
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Author : Wojciech Broniowski
language : en
Publisher: Wojciech Broniowski
Release Date : 2021-07-15
Explaining Neural Networks In Raw Python written by Wojciech Broniowski and has been published by Wojciech Broniowski this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-15 with Computers categories.
These lectures explain the very basic concepts of neural networks at a most elementary level, requiring only very rudimentary knowledge of Python, or actually any programming language. With simplicity in mind, the code for various algorithms of neural networks is written from absolute scratch, i.e. without any use of dedicated higher-level libraries. That way one can follow all the programming steps in an explicit manner. The book is intended for undergraduate students and for advanced high school pupils and their teachers.
Neural Networks In The Analysis And Design Of Structures
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Author : Zenon Waszczysznk
language : en
Publisher: Springer
Release Date : 2014-05-04
Neural Networks In The Analysis And Design Of Structures written by Zenon Waszczysznk and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-04 with Computers categories.
Neural Networks are a new, interdisciplinary tool for information processing. Neurocomputing being successfully introduced to structural problems which are difficult or even impossible to be analysed by standard computers (hard computing). The book is devoted to foundations and applications of NNs in the structural mechanics and design of structures.
Guide To Convolutional Neural Networks
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Author : Hamed Habibi Aghdam
language : en
Publisher: Springer
Release Date : 2017-05-17
Guide To Convolutional Neural Networks written by Hamed Habibi Aghdam and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-17 with Computers categories.
This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website. This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.
Neural Networks Theory
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Author : Alexander I. Galushkin
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-10-29
Neural Networks Theory written by Alexander I. Galushkin 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 2007-10-29 with Technology & Engineering categories.
This book, written by a leader in neural network theory in Russia, uses mathematical methods in combination with complexity theory, nonlinear dynamics and optimization. It details more than 40 years of Soviet and Russian neural network research and presents a systematized methodology of neural networks synthesis. The theory is expansive: covering not just traditional topics such as network architecture but also neural continua in function spaces as well.
Artificial Neural Networks In Biological And Environmental Analysis
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Author : Grady Hanrahan
language : en
Publisher: CRC Press
Release Date : 2011-01-18
Artificial Neural Networks In Biological And Environmental Analysis written by Grady Hanrahan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-01-18 with Mathematics categories.
Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound
Stability Analysis Of Neural Networks And Evolving Intelligent Systems
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Author : Jose de Jesus Rubio
language : en
Publisher: Springer Nature
Release Date : 2025-04-30
Stability Analysis Of Neural Networks And Evolving Intelligent Systems written by Jose de Jesus Rubio and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-30 with Computers categories.
This book explores the stability analysis of neural networks and evolving intelligent systems, focusing on their ability to adapt to changing environments. It differentiates between neural networks, which have a static structure and dynamic parameter learning, and evolving intelligent systems, where both structure and parameters are dynamic. A key concern addressed is ensuring the stability of these systems, as instability can lead to damage or accidents in online applications. Stability Analysis of Neural Networks and Evolving Intelligent Systems emphasizes that stable algorithms used in these systems must be compact, effective, and stable. The book is divided into two parts: the first five chapters cover stability analysis of neural networks, while the latter five chapters explore stability analysis of evolving intelligent systems. The Lyapunov method is the primary tool used for these analyses. Neural networks are applied to various modeling and prediction tasks, including warehouse load distribution, wind turbine behavior, crude oil blending, and beetle population dynamics. Evolving intelligent systems are applied to modeling brain and eye signals, nonlinear systems with dead-zone input, and the Box Jenkins furnace. Each chapter introduces specific techniques and algorithms, such as a backpropagation algorithm with a time-varying rate for neural networks, analytic neural network models for wind turbines, and self-organizing fuzzy modified least square networks (SOFMLS) for evolving systems. The book also addresses challenges like incomplete data and big data learning, proposing hybrid methods and modified algorithms to improve performance and stability. The effectiveness of the proposed techniques is verified through simulations and comparisons with existing methods.
Neural Networks
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Author : Gérard Dreyfus
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-11-25
Neural Networks written by Gérard Dreyfus 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 2005-11-25 with Science categories.
Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.
The Handbook Of Brain Theory And Neural Networks
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Author : Michael A. Arbib
language : en
Publisher: MIT Press
Release Date : 2003
The Handbook Of Brain Theory And Neural Networks written by Michael A. Arbib and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computers categories.
This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).
Generative Ai Explained Your Essential Guide
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Author : Anand Vemula
language : en
Publisher: Anand Vemula
Release Date :
Generative Ai Explained Your Essential Guide written by Anand Vemula and has been published by Anand Vemula this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
"Generative AI Explained: Your Essential Guide" offers a clear and concise introduction to the world of generative artificial intelligence (AI). Authored by experts in the field, this handbook provides readers with a comprehensive understanding of the principles, applications, and implications of generative AI technology. The book begins by demystifying the concept of generative AI, explaining how it differs from other forms of AI and detailing its underlying mechanisms. Readers are guided through key concepts such as neural networks, machine learning, and deep learning, gaining insights into how these technologies enable machines to generate new content autonomously. From there, the handbook delves into practical applications of generative AI across various industries. Through real-world examples and case studies, readers discover how generative AI is being used to create art, music, literature, and even video games. Additionally, the book explores its applications in fields like healthcare, finance, and marketing, highlighting the transformative impact it is having on society. Furthermore, the handbook addresses important ethical and societal considerations surrounding generative AI. Readers are encouraged to critically examine issues such as bias, privacy, and the implications of AI-generated content on human creativity and employment. Throughout the book, complex technical concepts are explained in a clear and accessible manner, making it suitable for readers with varying levels of technical expertise. Whether you're a student, researcher, developer, or simply curious about the future of AI, "Generative AI Explained" serves as an invaluable resource for understanding the fundamentals of this rapidly evolving field. In summary, "Generative AI Explained: Your Essential Guide" offers a comprehensive yet approachable exploration of generative artificial intelligence, equipping readers with the knowledge and insights needed to navigate the increasingly AI-driven world.