Neural Network Computing

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Neural Network Computing

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Author : Ramachandran Bharath
language : en
Publisher: TAB/Electronics
Release Date : 1994-01-01

Neural Network Computing written by Ramachandran Bharath and has been published by TAB/Electronics this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-01-01 with Artificial intelligence categories.

Neural Network Computing

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Author : Ramachandran Bharath
language : en
Publisher: McGraw-Hill Companies
Release Date : 1994

Neural Network Computing written by Ramachandran Bharath and has been published by McGraw-Hill Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computers categories.

An introduction to neural networking for systems designers, software developers, programmers, and advanced hobbyists. The authors explain how "brain-style" computing will revolutionize information processing in the 21st century. The disk includes programs for simulating artificial neural networks.

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.

Artificial Neural Networks

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Author : V. Rao Vemuri
language : en
Publisher:
Release Date : 1988

Artificial Neural Networks written by V. Rao Vemuri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Computers categories.

This volume provides an introduction to the field of artificial neural networks, and their role in the emerging field of neurocomputing, and the theoretical concepts that are the focus of current research. The genesis of this subject can be traced back to the 1940s, while present interest is due to recent developments in theoretical models, technologies, and algorithms. The papers selected for this volume were published primarily in IEEE journals.

Learning Deep Learning

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Author : Magnus Ekman
language : en
Publisher: Addison-Wesley Professional
Release Date : 2021-07-19

Learning Deep Learning written by Magnus Ekman and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-19 with Computers categories.

NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA "Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us." -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Journal Of Neural Network Computing

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Author :
language : en
Publisher:
Release Date : 1991

Journal Of Neural Network Computing written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Neural networks (Computer science) categories.

Neural Network Computing For The Electric Power Industry

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Author : Dejan J. Sobajic
language : en
Publisher: Psychology Press
Release Date : 2013-06-17

Neural Network Computing For The Electric Power Industry written by Dejan J. Sobajic and has been published by Psychology Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-17 with Psychology categories.

Power system computing with neural networks is one of the fastest growing fields in the history of power system engineering. Since 1988, a considerable amount of work has been done in investigating computing capabilities of neural networks and understanding their relevance to providing efficient solutions for outstanding complex problems of the electric power industry. A principal objective of a power utility is to provide electric energy to its customers in a secure, reliable and economic manner. Toward this aim, utility personnel are engaged in a variety of activities in areas of supervisory control and monitoring, evaluation of operating conditions, operation planning and scheduling, system development, equipment testing, etc. Over the past decades significant advances have been made in the development of new concepts, design of hardware and software systems, and implementation of solid-state devices which all contributed to the steadily improving power system performance that we are experiencing today. Advanced information processing technologies played an important role in these development efforts. Members of the Special Interest Group for Power Engineering of the INNS recognized the need for bringing together leading researchers in the field of neurocomputing with experts from power utilities and manufacturing companies to assess the current state of affairs and to explore the directions of further research and practice. This book is based on The Summer Workshop on Neural Network Computing for the Electric Power Industry which brought together approximately forty specialists with backgrounds in power engineering, system operation and planning, neural network theory and AI systems design. An informal and highly inspiring atmosphere of the workshop facilitated open discussion and exchange of expertise between the participants.

Neural Networks And Computing

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Author : Tommy W. S. Chow
language : en
Publisher: Imperial College Press
Release Date : 2007

Neural Networks And Computing written by Tommy W. S. Chow and has been published by Imperial College Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.

This book covers neural networks with special emphasis on advanced learning methodologies and applications. It includes practical issues of weight initializations, stalling of learning, and escape from a local minima, which have not been covered by many existing books in this area. Additionally, the book highlights the important feature selection problem, which baffles many neural networks practitioners because of the difficulties handling large datasets. It also contains several interesting IT, engineering and bioinformatics applications.

Neural Network Computer Vision With Opencv 5

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Author : Gopi Krishna Nuti
language : en
Publisher: BPB Publications
Release Date : 2023-12-30

Neural Network Computer Vision With Opencv 5 written by Gopi Krishna Nuti and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-30 with Computers categories.

Unlocking computer vision with Python and OpenCV KEY FEATURES ● Practical solutions to image processing challenges. ● Detect and classify objects in images. ● Recognize faces and text from images using character detection and recognition models. DESCRIPTION Neural Network Computer Vision with OpenCV equips you with professional skills and knowledge to build intelligent vision systems using OpenCV. It creates a sequential pathway for understanding morphological operations, edge and corner detection, object localization, image classification, segmentation, and advanced applications like face detection and recognition, and optical character recognition. This book offers a practical roadmap to explore the nuances of image processing with detailed discussions on each topic, supported by hands-on Python code examples. The readers will learn the basics of neural networks, deep learning and CNNs by using deep learning frameworks like Keras, Tensorflow, PyTorch, Caffe etc. They will be able to utilize OpenCV DNN module to classify images by using models like Inception V3, Resnet 101, Mobilenet V2. Moreover, the book will help to successfully Implement object detection using YOLOv3, SSD and R-CNN models. The character detection and recognition models are also covered in depth with code examples. You will gain a deeper understanding of how these techniques impact real-world scenarios and learn to harness the potential of Python and OpenCV to solve complex problems. Whether you are building intelligent systems, automating processes, or working on image-related projects, this book equips you with the skills to revolutionize your approach to visual data. WHAT YOU WILL LEARN ● Acquire expertise in image manipulation techniques. ● Apply knowledge to practical scenarios in computer vision. ● Implement robust systems for face detection and recognition. ● Enhance projects with accurate object localization capabilities. ● Extract text information from images effectively. WHO THIS BOOK IS FOR This book is designed for those with basic Python skills, from beginners to intermediate-level readers. Whether you are building intelligent robots that perceive their surroundings or crafting advanced vision systems for object detection and image analysis, this book will equip you with the tools and skills to push the boundaries of AI perception. TABLE OF CONTENTS 1. Introduction to Computer Vision 2. Basics of Imaging 3. Challenges in Computer Vision 4. Classical Solutions 5. Deep Learning and CNNs 6. OpenCV DNN Module 7. Modern Solutions for Image Classification 8. Modern Solutions for Object Detection 9. Faces and Text 10. Running the Code 11. End-to-end Demo

An Introduction To Neural Computing

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Author : Igor Aleksander
language : en
Publisher:
Release Date : 1995

An Introduction To Neural Computing written by Igor Aleksander and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.

The second edition of this text has been updated and includes material on new developments including neurocontrol, pattern analysis and dynamic systems. The book should be useful for undergraduate students of neural networks.