Neural Network Clasroom

Neural Network Design (2nd Edition) [Repost]  eBooks & eLearning

Posted by AlexGolova at Dec. 20, 2018
Neural Network Design (2nd Edition) [Repost]

Neural Network Design (2nd Edition) by Martin T Hagan
English | 1 Sept. 2014 | ISBN: 0971732116 | 800 Pages | PDF | 11.27 MB

Neural Network Learning: Theoretical Foundations  eBooks & eLearning

Posted by insetes at Jan. 12, 2020
Neural Network Learning: Theoretical Foundations

Neural Network Learning: Theoretical Foundations By Martin Anthony, Peter L. Bartlett
2009 | 404 Pages | ISBN: 052111862X | PDF | 10 MB
Information Processing by Biochemical Systems: Neural Network-Type Configurations (Repost)

Orna Filo, Noah Lotan, "Information Processing by Biochemical Systems: Neural Network-Type Configurations"
English | 2009 | ISBN: 0470500948 | PDF | pages: 169 | 1.0 mb

Learn Artificial Neural Network (Engineering Tutorials)  eBooks & eLearning

Posted by Free butterfly at June 19, 2021
Learn Artificial Neural Network (Engineering Tutorials)

Learn Artificial Neural Network (Engineering Tutorials) by Ajay Patel
English | 2020 | ISBN: N/A | ASIN: B08LYKF7FC | 97 pages | MOBI | 0.92 Mb

Advances in Neural Network Research and Applications (Repost)  eBooks & eLearning

Posted by lenami at Nov. 12, 2010
Advances in Neural Network Research and Applications (Repost)

Advances in Neural Network Research and Applications
Publisher: Springer | ISBN: 3642129897 | edition 2010 | PDF | 936 pages | 15 mb

This book is a part of the Proceedings of the Seventh International Symposium on Neural Networks (ISNN 2010), held on June 6-9, 2010 in Shanghai, China. ISNN 2010 received numerous submissions from about thousands of authors in about 40 countries and regions across six continents .
Based on the rigorous peer-reviews by the program committee members and the reviewers, 108 high-quality papers were selected for publications in Lecture Notes in Electrical Engineering (LNEE) Proceedings. These papers cover all major topics of the engineering designs and applications of neural network research. In addition to the contributed papers, the ISNN 2010 technical program included four plenary speeches by Andrzej Cichocki (RIKEN Brain Science Institute, Japan), Chin-Teng Lin (National Chiao Tung University, Taiwan), DeLiang Wang (Ohio State University, USA), Gary G. Yen (Oklahoma State University, USA).

Building your own Neural Network from Scratch with Python  eBooks & eLearning

Posted by BlackDove at Nov. 14, 2021
Building your own Neural Network from Scratch with Python

Building your own Neural Network from Scratch with Python
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.2 GB | Duration: 12h 28m



Master how Machine Learning and Deep Learning algorithms and libraries work under the hood with practical examples.

The Basics Of Neural Network  eBooks & eLearning

Posted by naag at July 3, 2019
The Basics Of Neural Network

The Basics Of Neural Network
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hour 15M | 449 MB
Genre: eLearning | Language: English
Coyote-Wolf Optimization-Based Deep Neural Network for Cancer Classification Using Gene Expression Profiles

Coyote-Wolf Optimization-Based Deep Neural Network for Cancer Classification Using Gene Expression Profiles by Mandar Deshmukh
English | 2022 | ISBN: N/A | ASIN: B0BG1LM383 | 320 pages | MOBI | 3.79 Mb
Dynamic Modeling and Neural Network-Based Intelligent Control of Flexible Systems

Dynamic Modeling and Neural Network-Based Intelligent Control of Flexible Systems
English | 2025 | ISBN: 1394255276 | 253 Pages | PDF | 15 MB

Neural Network Learning: Theoretical Foundations (repost)  eBooks & eLearning

Posted by interes at July 14, 2013
Neural Network Learning: Theoretical Foundations (repost)

Martin Anthony, Peter L. Bartlett, "Neural Network Learning: Theoretical Foundations"
English | ISBN 10: 052111862X | 2009 | PDF | 404 pages | 9,3 MB

This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions.