Dawani Hands-On Mathematics for Deep Learning

Build a solid mathematical foundation for training efficient deep neural networks
1. Auflage 2020
ISBN: 978-1-83864-184-9
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 364 Seiten

No detailed description available for "Hands-On Mathematics for Deep Learning".

Autoren/Hrsg.

Dawani, Jay

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software

Inhaltsverzeichnis

Table of Contents - Linear Algebra
- Vector Calculus
- Probability and Statistics
- Optimization
- Graph Theory
- Linear Neural Networks
- Feedforward Neural Networks
- Regularization
- Convolutional Neural Networks
- Recurrent Neural Networks
- Attention Mechanisms
- Generative Models
- Transfer and Meta Learning
- Geometric Deep Learning

Über Autor(innen)

Dawani Jay:

Jay Dawani is a former professional swimmer turned mathematician and computer scientist. He is also a Forbes 30 Under 30 Fellow. At present, he is the Director of Artificial Intelligence at Geometric Energy Corporation (NATO CAGE) and the CEO of Lemurian Labs - a startup he founded that is developing the next generation of autonomy, intelligent process automation, and driver intelligence. Previously he has also been the technology and R&D advisor to Spacebit Capital. He has spent the last three years researching at the frontiers of AI with a focus on reinforcement learning, open-ended learning, deep learning, quantum machine learning, human-machine interaction, multi-agent and complex systems, and artificial general intelligence.