PirateBay Download | reinforcement learning python explained beginners

reinforcement learning python explained beginners

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Uploaded On:: Jan. 2, 2021, 4:38 p.m.
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Info Hash:: 12B5E4E95F5C7115E18481ADF639649B1BB54C36

Description Reinforcement Learning (RL) possesses immense potential and is doubtless one of the most dynamic and stimulating fields of research in Artificial Intelligence. RL is considered as a game-changer in Data Science, particularly after observing the winnings of AI agents AlphaGo Zero and OpenAI Five against top human champions. However, RL is not restricted to games. The progress in Reinforcement Learning, especially during the last few years, has been sensational. RL is everywhere now, ranging from resource management to chemistry, from healthcare to finance, and from Recommender Systems to more advanced applications in stock prediction. Since RL is goal-oriented learning, an understanding of RL is not only vital but also indispensable in all the fields of Data Science. This course will enable you to take your career to the next level, as it presents you with a clear explanation of the concepts and implementations of RL in Data Science. The course ‘Reinforcement Learning, Theory and Practice’ provides you with an opportunity for innovative, independent learning. The course focuses on the practical applications of RL and includes a hands-on project. The course is: · Easy to understand. · Descriptive. · Comprehensive. · Practical with live coding. · Rich with advanced and the most recently discovered RL models by the champions in this field. This course is designed for beginners, although complex concepts are covered later. As this course is a compilation of all the basics, it will inspire you to move forward and experience much more than what you have learned. You will be assigned homework/ tasks/ activities at the end of each module, which will assess / (further build) your learning based on the concepts and methods you have learned earlier on. Since the aim is to get you up and running with implementations, many of these activities will be coding based. Data Science is unquestionably a rewarding career. You get to solve some of the most interesting problems, and you are rewarded with a handsome salary package. A core understanding of RL will empower you with more AI tools and ensure progressive career growth. As we have already said, RL possesses immense potential. Don’t miss out on this opportunity to learn the advanced concepts and methodologies of RL at a highly competitive price. The tutorials are subdivided into 75+ short HD videos along with detailed code notebooks. Teaching is our passion: Our online tutorials have been created with the best possible expertise to help you in understanding the RL concepts clearly. We have taken great care to ensure the code base is up to date. We really want you to accomplish a strong basic understanding of RL before you move onward to the advanced version. The perks of this compelling course include high-quality video content, assessment questions, meaningful course material, course notes, and handouts. You can also approach our team whenever you have any queries. Course Content: This all-inclusive course consists of the following topics: 1. Introduction a. Motivation i. What is Reinforcement Learning? ii. How is it different from other Machine Learning Frameworks? iii. Real-world examples iv. Exercises and Thoughts b. Terminology of Reinforcement Learning i. Agent ii. Environment iii. Action iv. State v. Transition vi. Reward vii. Policy viii. Exercises and Thoughts c. Example Grid World i. Deterministic World ii. Stochastic World iii. Stationary World iv. Non-Stationary World v. Exercises and Thoughts 2. Markov Decision Process (MDP) a. Prerequisites i. Probability Theory Review ii. Modeling Uncertainty of Environment iii. Running Averages iv. Simulation in Python v. Exercises and Thoughts b. Elements of an MDP i. Input: State Space ii. Input: Action Space iii. Input: Environment Model iv. Input: Reward function v. Output: Policy vi. Worked Examples vii. Exercises and Thoughts c. More on Rewards i. Delayed Reward ii. Reward Scaling iii. Policy Changes with Reward Scaling: Worked Example iv. Infinite Horizons and Stationarity v. Walks or Sequences vi. Value of a Walk vii. Stationarity of Preferences viii. Discounted Rewards ix. Exercises and Thoughts d. Solving an MDP i. Bellman Optimization Criteria ii. Model-Based Value Iterations iii. Optimal Value Function iv. Finding Optimal Policy v. Model-Based Policy Iterations vi. Action-Value Functions vii. Relationship Between Value Functions and Action-Value Functions viii. Policy Evaluation ix. Learner Evaluation x. Exercises and Thoughts 3. Model Free Learning a. Value Approximation i. Episodes ii. Running-Averages Applications iii. Incremental Learning iv. Properties of Learning Rates v. Simulation in Python vi. Exercises and Thoughts b. Temporal Difference (TD) Learning i. What is Temporal Difference? ii. TD (1) Update Rule iii. Eligibility Traces iv. TD (1) Learning Algorithm v. Implementation in Python vi. Limitations of TD (1) vii. Exercises and Thoughts c. Toward TD(λ) i. Maximum Likelihood Estimate ii. TD (0) Update Rule iii. TD (λ) iv. K-Step Look-a-head v. Combinations of Different Step Look-a-heads vi. Good Values of λ vii. TD (λ) Algorithm viii. Implementation in Python ix. Exercises and Thoughts d. Q-Learning i. Q-functions ii. Contraction Mapping iii. Bellman Operators iv. Why Value Iteration Works? v. Q-Learning Algorithm vi. Implementation in Python vii. Exercises and Thoughts e. Policy Iteration i. Direct Policy Learning ii. Value Estimation in Policy Iteration iii. Why Policy Iteration Works iv. Policy Iteration Algorithm v. Implementation in Python vi. Exercises and Thoughts 4. Project a. Game in OpenAI GYM 5. What Next? a. Game Theory b. How to Model Infinite States and Actions? c. Deep Reinforcement Learning After completing this course successfully, you will be able to: Understand how RL techniques are applied to resolve real-world problems. Understand the methodology of RL with Data Science using interesting examples. Complete a project on the OpenAI Gym toolkit. Who this course is for: People who want to get their data speak. People who want to learn RL with real applications in Data Science. Individuals who are passionate about numbers and programming. People who want to learn Reinforcement Learning along with its implementation in realistic projects. Beginners in the field of Data Science and Artificial Intelligence Requirements No prior knowledge is needed. You will start from the basics and gradually build your knowledge in the subject. A willingness to learn and practice. Knowledge of Python will be a plus. Last Updated 12/2020

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[TutsNode.com] - Reinforcement Learning with Python Explained for Beginners/7. More on Reward/5. MOR Reward Scaling.mp4 31.4 MB
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[TutsNode.com] - Reinforcement Learning with Python Explained for Beginners/9. Value Approximation/5. Monte-Carlo Learning.mp4 25.6 MB
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[TutsNode.com] - Reinforcement Learning with Python Explained for Beginners/12. Project Frozenlake (Open AI Gym)/1. Frozenlake 1.mp4 8.9 MB
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[TutsNode.com] - Reinforcement Learning with Python Explained for Beginners/12. Project Frozenlake (Open AI Gym)/3. Implementation Frozen Lake Numpy Activity.mp4 5.7 MB
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[TutsNode.com] - Reinforcement Learning with Python Explained for Beginners/11. TD Lambda/6. TD Q Learning TD Lambda TD(Lambda) MAPRover Activity.mp4 5.6 MB
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[TutsNode.com] - Reinforcement Learning with Python Explained for Beginners/10. Temporal Differencing-Q Learning/11. SARSA MAPRover Activity.mp4 5.6 MB
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[TutsNode.com] - Reinforcement Learning with Python Explained for Beginners/12. Project Frozenlake (Open AI Gym)/1.1 Reinforcement Learning Complete.pptx 5.2 MB

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