Understanding 99 Of Artificial Neural Networks

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Understanding Ninety Nine Percent Of Artificial Neural Networks

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Author : Marcello Bosque
language : en
Publisher: iUniverse
Release Date : 2002

Understanding Ninety Nine Percent Of Artificial Neural Networks written by Marcello Bosque and has been published by iUniverse this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Neural networks (Computer science) categories.

Understanding 99 Of Artificial Neural Networks

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Author : Marcelo Bosque
language : en
Publisher: Writers Club Press
Release Date : 2002-03

Understanding 99 Of Artificial Neural Networks written by Marcelo Bosque and has been published by Writers Club Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-03 with Computers categories.

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.

Artificial Intelligence Techniques For Networked Manufacturing Enterprises Management

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Author : Lyes Benyoucef
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-05-10

Artificial Intelligence Techniques For Networked Manufacturing Enterprises Management written by Lyes Benyoucef 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 2010-05-10 with Computers categories.

Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management addresses prominent concepts and applications of AI technologies in the management of networked manufacturing enterprises. The aim of this book is to align latest practices, innovation and case studies with academic frameworks and theories, where AI techniques are used efficiently for networked manufacturing enterprises. More specifically, it includes the latest research results and projects at different levels addressing quick-response system, theoretical performance analysis, performance and capability demonstration. The role of emerging AI technologies in the modelling, evaluation and optimisation of networked enterprises’ activities at different decision levels is also covered. Artificial Intelligence Techniques for Networked Manufacturing Enterprises Management is a valuable guide for postgraduates and researchers in industrial engineering, computer science, automation and operations research.

Artificial Intelligence Illuminated

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Author : Ben Coppin
language : en
Publisher: Jones & Bartlett Learning
Release Date : 2004

Artificial Intelligence Illuminated written by Ben Coppin and has been published by Jones & Bartlett Learning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computers categories.

Artificial Intelligence Illuminated presents an overview of the background and history of artificial intelligence, emphasizing its importance in today's society and potential for the future. The book covers a range of AI techniques, algorithms, and methodologies, including game playing, intelligent agents, machine learning, genetic algorithms, and Artificial Life. Material is presented in a lively and accessible manner and the author focuses on explaining how AI techniques relate to and are derived from natural systems, such as the human brain and evolution, and explaining how the artificial equivalents are used in the real world. Each chapter includes student exercises and review questions, and a detailed glossary at the end of the book defines important terms and concepts highlighted throughout the text.

Understanding Intelligence

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Author : Rolf Pfeifer
language : en
Publisher: MIT Press
Release Date : 2001-07-27

Understanding Intelligence written by Rolf Pfeifer and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-07-27 with Computers categories.

The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run "programs"; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior—thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI," and "behavior-based AI." This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building. The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.

Artificial Neural Networks Icann 2007

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Author : Joaquim Marques de Sá
language : en
Publisher: Springer
Release Date : 2007-09-14

Artificial Neural Networks Icann 2007 written by Joaquim Marques de Sá and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09-14 with Computers categories.

This book is the second of a two-volume set that constitutes the refereed proceedings of the 17th International Conference on Artificial Neural Networks, ICANN 2007. It features contributions related to computational neuroscience, neurocognitive studies, applications in biomedicine and bioinformatics, pattern recognition, self-organization, text mining and internet applications, signal and times series processing, vision and image processing, robotics, control, and more.

Understanding The Artificial On The Future Shape Of Artificial Intelligence

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Author : Massimo Negrotti
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Understanding The Artificial On The Future Shape Of Artificial Intelligence written by Massimo Negrotti 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 2012-12-06 with Computers categories.

In recent years a vast literature has been produced on the feasibility of Artificial Intelligence (AI). The topic most frequently discussed is the concept of intelligence, with efforts to demonstrate that it is or is not transferable to the computer. Only rarely has attention been focused on the concept of the artificial per se in order to clarify what kind, depth and scope of performance (including intelligence) it could support. Apart from the classic book by H.A. Simon, The Sciences of the Artificial, published in 1969, no serious attempt has been made to define a conceptual frame for understanding the intimate nature of intelligent machines independently of its claimed or denied human-like features. The general aim of this book is to discuss, from different points of view, what we are losing and what we are gaining from the artificial, particularly from AI, when we abandon the original anthropomorphic pretension. There is necessarily a need for analysis of the history of AI and the limits of its plausibility in reproducing the human mind. In addition, the papers presented here aim at redefining the epistemology and the possible targets of the AI discipline, raising problems, and proposing solutions, which should be understood as typical of the artificial rather than of an information-based conception of man.

The Deep Learning Architect S Handbook

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Author : Ee Kin Chin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-12-29

The Deep Learning Architect S Handbook written by Ee Kin Chin 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 2023-12-29 with Computers categories.

Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycle Key Features Interpret your models’ decision-making process, ensuring transparency and trust in your AI-powered solutions Gain hands-on experience in every step of the deep learning life cycle Explore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerations Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDeep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives. This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You’ll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency. As you progress, you’ll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You’ll also discover the transformative potential of large language models (LLMs) for a wide array of applications. By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.What you will learn Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs) Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your model Deal with multi-modal data drift in a production environment Evaluate the quality and bias of your models Explore techniques to protect your model from adversarial attacks Get to grips with deploying a model with DataRobot AutoML Who this book is for This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.

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.

Understanding And Bridging The Gap Between Neuromorphic Computing And Machine Learning Volume Ii

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Author : Huajin Tang
language : en
Publisher: Frontiers Media SA
Release Date : 2024-08-26

Understanding And Bridging The Gap Between Neuromorphic Computing And Machine Learning Volume Ii written by Huajin Tang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-26 with Science categories.

Towards the long-standing dream of artificial intelligence, two solution paths have been paved: (i) neuroscience-driven neuromorphic computing; (ii) computer science-driven machine learning. The former targets at harnessing neuroscience to obtain insights for brain-like processing, by studying the detailed implementation of neural dynamics, circuits, coding and learning. Although our understanding of how the brain works is still very limited, this bio-plausible way offers an appealing promise for future general intelligence. In contrast, the latter aims at solving practical tasks typically formulated as a cost function with high accuracy, by eschewing most neuroscience details in favor of brute force optimization and feeding a large volume of data. With the help of big data (e.g. ImageNet), high-performance processors (e.g. GPU, TPU), effective training algorithms (e.g. artificial neural networks with gradient descent training), and easy-to-use design tools (e.g. Pytorch, Tensorflow), machine learning has achieved superior performance in a broad spectrum of scenarios. Although acclaimed for the biological plausibility and the low power advantage (benefit from the spike signals and event-driven processing), there are ongoing debates and skepticisms about neuromorphic computing since it usually performs worse than machine learning in practical tasks especially in terms of the accuracy.