logo

EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link:  https://ebookbell.com/faq 


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Deep Learning Applications 1st Edition M Arif Wani Mehmed Kantardzic

  • SKU: BELL-11029214
Deep Learning Applications 1st Edition M Arif Wani Mehmed Kantardzic
$ 35.00 $ 45.00 (-22%)

4.7

16 reviews

Deep Learning Applications 1st Edition M Arif Wani Mehmed Kantardzic instant download after payment.

Publisher: Springer
File Extension: PDF
File size: 9.75 MB
Pages: 190
Author: M. Arif Wani, Mehmed Kantardzic, Moamar Sayed-Mouchaweh
ISBN: 9789811518157, 9811518157
Language: English
Year: 2020
Edition: 1

Product desciption

Deep Learning Applications 1st Edition M Arif Wani Mehmed Kantardzic by M. Arif Wani, Mehmed Kantardzic, Moamar Sayed-mouchaweh 9789811518157, 9811518157 instant download after payment.

This book presents a compilation of selected papers from the 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2018), focusing on use of deep learning technology in application like game playing, medical applications, video analytics, regression/classification, object detection/recognition and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

Related Products