Full Description
Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of industry, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms.
Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering.
Contents
Part 1: AI and Machine Learning
1. Artificial Intelligence
2. Machine Learning
3. Regression Analysis
4. Bayesian Statistics
5. Learning Theory
6. Supervised Learning
7. Unsupervised Learning
8. Reinforcement Learning
9. Instance Based Learning and Feature Engineering
Part 2: Data Science and Predictive Analysis
10. Introduction to Data Science and Analysis
11. Linear Algebra, Statistics, Probability, Hypothesis and Inference, Gradient Descent
12. Predictive Analysis
Part 3: Edge Computing
13. Distributed Computing - Cloud to fog to Edge
14. Edge Computing
15. Integrating AI with Edge Computing
16. Machine learning integration with Edge Computing
17. Applying AI/Ml at the edge
