Practical Multi-Armed Bandit Algorithms in Python . Modelling real business problems as MAB and implementing digital AI agents to automate them . Understanding the challenge of RL regarding the exploration-exploitation dilema. Understanding the challenges of RL in terms of the design of the reward functions and sample efficiency. Estimation of action values through incremental sampling. Estimating of action . Estimating the value of actions by incremental sampling . Emphasizing the importance of the Optimistic Initialization strategy. Establishing the value for each action in an attempt to solve the problem of exploration and exploitation. Emphasize the need to be able to identify and identify the problem as a problem.API quota exceeded. You can make 500 requests per day.
Who this course is for:
- Anyone with a basic Python skills desiring to the started in Reinforcement Learning.
- Experienced AI Engineers, ML Engineers, Data Scientist, and Software Engineers wanting to apply Reinforcement Learning to real business problems.
- Business professionals willing to learn how Reinforcement Learning can help with automating adaptive decision making processes.
|File Name :||Practical Multi-Armed Bandit Algorithms in Python free download|
|Genre / Category:||Development|
|File Size :||5.97 gb|
|Publisher :||Edward Pie|
|Updated and Published:||08 Aug,2022|