Computational Optimizations for Machine Learning

FREDDY GABBAY

ISBN 10: 3036531866 / ISBN 13: 9783036531861

Published by MDPI AG, 2022

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - The present book contains the 10 articles finally accepted for publication in the Special Issue 'Computational Optimizations for Machine Learning' of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more.It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity.

Seller Inventory # 9783036531861

Bibliographic Details

• Title: Computational Optimizations for Machine Learning
• Publisher: MDPI AG
• Publication Date: 2022
• Language: English
• ISBN 10: 3036531866
• ISBN 13: 9783036531861
• Binding: Buch
• Condition: Neu
• Dimensions: 250W x 175H x 22D millimeters 250 millimeters width by 175 millimeters height by 22 millimeters depth
• Weight: 890 grams

About this title

Synopsis

The present book contains the 10 articles finally accepted for publication in the Special Issue "Computational Optimizations for Machine Learning" of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more.

It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity.