- We will send in 10–14 business days.
/images/books/13011095/9783036531861.jpg)
- Publisher: MDPI AG
- ISBN-10: 3036531866
- ISBN-13: 9783036531861
- Format: 17 x 24.4 x 2.2 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
Computational Optimizations for Machine Learning (e-book) (used book) | bookbook.eu
Reviews
Description
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.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 12d.03:00:24
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Publisher: MDPI AG
- ISBN-10: 3036531866
- ISBN-13: 9783036531861
- Format: 17 x 24.4 x 2.2 cm, hardcover
- Language: English English
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.
Reviews