menuPrinciples of Reinforcement LearningLeft Brain
Objective
This class is intended for people whom want to better understand reinforcement learning(RL). RL utilizes efficient trial and error techniques to find optimal solutions. RL techniques have been recently deployed in optimizing supervised network hyperparameters, solving games, economic models and multidimensional real world problems.
Agenda
- AI Landscape and how RL fits into it
- Basics of Reinforcement Learning
- Policy Gradient Techniques
- Leading papers and current research
- Self-play and Gameplay
Intended Audience
Programmers, managers, investors, enthusiast pretty much anyone technically curious about deploying artificial intelligence. If you are familiar with supervised learning but did not know how to implement them in tensorflow.
What do I need to know to be successful:
All you need is curiosity in the subject and access to a laptop for which you have administrator access. (Specifically so you can turn off firewall protection in case you have issues connecting to the cloud).
We will grant you access to a Google Cloud servet with a connected GPU for conducting the hands-on exercise.
About 35% of the class is lectures and 65% is hands on programming.
In order to do the hands-on programming segment you need to know python.
A general understanding of numpy, scipy and tensorflow would help. But since people have varying experience with these topics, we will be providing a quick overview.
info@leftbrain.consulting