Tag: Dimensionality

Free Download Relativity and the Dimensionality of the World by Vesselin Petkov
English | PDF (True) | 2007 | 283 Pages | ISBN : 1402063172 | 4.4 MB
All physicists would agree that one of the most fundamental problems of the 21st century physics is the dimensionality of the world. In the four-dimensional world of Minkowski (or Minkowski spacetime) the most challenging problem is the nature of the temporal dimension. In Minkowski spacetime it is merely one of the four dimensions, which means that it is entirely given like the other three spacial dimensions. If the temporal dimension were not given in its entirety and only one constantly changing moment of it existed, Minkowski spacetime would be reduced to the ordinary three-dimensional space.

Free Download Clustering And Dimensionality Reduction – Deep Dive
Published 1/2024
Created by Dalibor Veljkovic
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 203 Lectures ( 27h 40m ) | Size: 10.4 GB

Free Download Feature and Dimensionality Reduction for Clustering with Deep Learning
English | 2024 | ISBN: 3031487427 | 460 Pages | PDF EPUB (True) | 34 MB
This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by "family" to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.