Applications Of Artificial Neural Networks In Image Processing Viii PDF Download

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Artificial Neural Networks in Pattern Recognition

Artificial Neural Networks in Pattern Recognition
Author: Luca Pancioni
Publisher: Springer
Total Pages: 415
Release: 2018-08-29
Genre: Computers
ISBN: 3319999788

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This book constitutes the refereed proceedings of the 8th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2018, held in Siena, Italy, in September 2018. The 29 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 35 submissions. The papers present and discuss the latest research in all areas of neural network- and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications. Chapter "Bounded Rational Decision-Making with Adaptive Neural Network Priors" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.


Convolutional Neural Networks

Convolutional Neural Networks
Author: Fouad Sabry
Publisher: One Billion Knowledgeable
Total Pages: 169
Release: 2023-06-26
Genre: Computers
ISBN:

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What Is Convolutional Neural Networks In the field of deep learning, a convolutional neural network, also known as a CNN, is a type of artificial neural network that is typically used to conduct analysis on visual data. At least one of the layers in a CNN substitutes the mathematical operation of convolution, sometimes known as convolving, for the more traditional matrix multiplication. They are utilized in both the image recognition and processing processes, as their primary purpose is the processing of pixel data. Applications can be found in areas such as image and video recognition, recommender systems, and more.image classification,image segmentation,image analysis for medical purposes,natural language processing,interfaces between the human brain and computers, andfinance time series. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Convolutional Neural Network Chapter 2: Artificial Neural Network Chapter 3: Types of Artificial Neural Networks Chapter 4: Deep Learning Chapter 5: Activation Function Chapter 6: Layer (Deep Learning) Chapter 7: LeNet Chapter 8: Tensor (Machine Learning) Chapter 9: Receptive Field Chapter 10: History of Artificial Neural Networks (II) Answering the public top questions about convolutional neural networks. (III) Real world examples for the usage of convolutional neural networks in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of convolutional neural networks. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.