explanation
This is a course provided by Coursera on the specialized topic of reinforcement learning transactions, such as reinforcement learning and specialization.
The four-subject learning enrichment course examines systems for adaptive learning and artificial intelligence (AI) trading. To utilize the full potential of artificial intelligence, we need to use reinforcement learning systems. solution reinforcement learning (RL) can solve real-world problems using the interaction of trial and error and the complete solution of reinforcement learning.
After completing this specialized course, you will understand many principles of modern statistics and artificial intelligence. Upon completion of this course, you will be required to pass advanced courses and apply artificial intelligence tools to solve real-world problems.
This course can be said to be world-renowned as the University of Alberta and the Artificial Intelligence Institute are major centers of artificial intelligence in Alberta. I recommend it. Buyers have given the course a rating of 4.7 out of 5. Over time, you can complete this course in 5 months by investing 5 hours a week. Perfect.
If you are teaching a course:
- Building a reinforcement learning system that makes continuous decisions
- Familiarity with reinforcement learning algorithms (Temporal – Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradients, Dyna, etc.)
- Understand tooling responsibilities and how to apply solutions as a problem in reinforcement learning.
- Understand the topic of how reinforcement learning can be used in machine learning, how to complete deep learning, and how monitoring and supervision of learning cannot help.
Profile Courses:
publisher : : Coursera
Level: Advanced
Instructors: Martha White, Adam White
Number of courses: 4 courses
Language:English
This course specializes in Reinforcement Learning
- Basics of Reinforcement Learning
- Sample-based learning method
- Prediction and control through function approximation
- Complete Reinforcement Learning System (Capstone)
Prerequisite subjects
It is recommended that learners have at least one year of undergraduate computer science or two to three years of professional experience in software development. Experience and comfort with Python programming is required. You should be proficient in translating algorithms and pseudocode into Python. Basic understanding of statistics (distributions, sampling, expected values), linear algebra (vectors and matrices), and calculus (differential calculus) concepts.
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sample video
installation manual
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Subtitles: English
Quality: 720p
download link
Password file: www.downloadly.ir
file size
2.61GB