Sewak / Karim / Pujari Practical Convolutional Neural Networks

Implement advanced deep learning models using Python
1. Auflage 2018
ISBN: 978-1-78839-414-7
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 218 Seiten

No detailed description available for "Practical Convolutional Neural Networks".

Autoren/Hrsg.

Sewak, Mohit

Karim, Md. Rezaul

Pujari, Pradeep

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein

Inhaltsverzeichnis

Table of Contents - Deep Neural Networks - Overview
- Introduction to Convolutional Neural Networks
- Build Your First CNN and Performance Optimization
- Popular CNN Model's Architectures
- Transfer Learning
- Autoencoders for CNN
- Object Detection with CNN
- Generative Adversarial Network
- Visual Attention Based CNN

Über Autor(innen)

Sewak Mohit:

Mohit is a Python programmer with a keen interest in the field of information security. He has completed his Bachelor's degree in technology in computer science from Kurukshetra University, Kurukshetra, and a Master's in engineering (2012) in computer science from Thapar University, Patiala. He is a CEH, ECSA from EC-Council USA. He has worked in IBM, Teramatrix (Startup), and Sapient. He currently doing a Ph.D. from Thapar Institute of Engineering & Technology under Dr. Maninder Singh. He has published several articles in national and international magazines. He is the author of Python Penetration Testing Essentials, Python: Penetration Testing for Developers and Learn Python in 7 days, also by Packt. For more details on the author, you can check the following user name mohitraj.csKarim Md. Rezaul:

Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.Pujari Pradeep:

https://www.linkedin.com/in/ppujari/