The Working with Deep Reinforcement Learning training course will cover the main ideas of deep reinforcement learning and some of the main tools and frameworks as well as leveraging widely-used Python-based libraries students may have encountered in machine learning spaces. Conventional Machine Learning works best when it is possible to find stable, representative labeled data from which it can find connections between the input features and the predictive outcomes. The effort to produce this labeled data is not always feasible or cost-effective.
The course begins with reinforcement learning systems that mimic the successes of established learning approaches found in the fields of neuroscience and animal conditioning research. Next, the remarkably successful techniques of Deep Learning systems used in dynamic environments evolve to include the ability of "learning to learn". The course concludes with generalizing much wider problem spaces including sophisticated gameplay, online ad-placement, digital resource management, optimized control systems, and self-driving vehicles.
In addition to covering the main ideas of deep reinforcement learning, we will cover some of the main tools and frameworks as well as leveraging widely-used Python-based libraries students have probably already run into in machine learning spaces.
Purpose
|
Learn about deep reinforcement learning, what it is, how it works, and how you can apply it to real-world problems. |
Audience
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Data Engineers, Data Scientists, and Software Engineers who need to work on learning systems that are more dynamic and sophisticated than simply learning from static data. |
Role
| Data Engineer - Data Scientist - Software Developer |
Skill Level
| Intermediate |
Style
| Fast Track - Targeted Topic - Workshops |
Duration
| 2 Days |
Related Technologies
| Deep Learning |
Productivity Objectives
- Define the concepts of agents, environments, states, actions, and rewards.
- Describe and use the major learning approaches of the fast-changing world of Deep reinforcement learning systems.
- Identify possible applications of deep reinforcement learning in your organization or industry.
