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Course Outline
Introduction
- Solving real-world problems through trial-and-error interactions
Understanding Adaptive Learning Systems and Artificial Intelligence (AI).
How Agents Perceive State
How to Reward an Agent
Case Study: Interacting with Website Visitors
Preparing the Environment for the Agent
Deep Dive into Reinforcement Learning Algorithms
Value-Based Methods vs Policy-Based Methods
Choosing a Reinforcement Learning Model
Using the Q-Learning Model-Free Reinforcement Learning Algorithm
Designing the Agent
Case Study: Smart Assistants
Interfacing the Agent to a Production Environment
Measuring the Results of Agent Actions
Troubleshooting
Summary and Conclusion
Requirements
- A genral understanding of reinforcement learning
- Experience with machine learning
- Java programming experience
Audience
- Data scientists
21 Hours
Testimonials (4)
That we got a complex overview also about the context - for example why do we need some annotations and what they mean. I liked the practical part of the training - having to manually run the commands and call the rest api's
Alina - ACCENTURE SERVICES S.R.L
Course - Quarkus for Developers
the trainer can clearly explain the topic and can answer every question.
Hannah Mae Lubigan - Security Bank Corporation
Course - Advanced Spring Boot
All to topic actually including API
RODULFO ALMEDA JR - DATAWORLD COMPUTER CENTER
Course - Introduction to JavaServer Faces
The breadth of the topis covered was quite a bit and the trainer tried to do justice to that.
