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Reinforcement learning and deep RL Python (theory and projects).
A comprehensive, hands-on, and easy-to-understand course on reinforcement learning. Learn about deep Q-Learning, SARSA, deep RL, car racing and trading projects, and be prepared with interview questions. About This Video Learn from a comprehensive yet self-explanatory course, divided into 145+ video...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
Packt Publishing,
[2022]
|
| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a A comprehensive, hands-on, and easy-to-understand course on reinforcement learning. Learn about deep Q-Learning, SARSA, deep RL, car racing and trading projects, and be prepared with interview questions. About This Video Learn from a comprehensive yet self-explanatory course, divided into 145+ videos along with detailed code notebooks Structured course with solid basic understanding and advanced practical concepts Up-to-date, practical explanations and live coding with Python to build six projects at an adequate pace In Detail Reinforcement learning is a subset of machine learning. In the RL training method, desired actions are rewarded, and undesired actions are punished. Deep RL is also a subfield of machine learning. In deep RL, intelligent machines and software are trained to learn from their actions in the same way that humans learn from experience. Deep RL has the capability to solve complex problems that were unmanageable by machines in the past. Therefore, the potential applications of deep RL in various sectors are enormous. We will start with an introduction to reinforcement learning and look at some case studies and real-world examples. Then you will look at Naïve/Random solutions and RL-based solutions. Next, you will see different types of RL solutions such as hyperparameters, Markov Decision Process, Q-Learning, and SARSA followed by a mini project on Frozen Lake. You will then learn deep learning/neural networks and deep RL/deep Q networks. Next, you will work on car racing and trading projects. Finally, you will go through some interview questions. By the end of this course, you will be able to relate the concepts and practical applications of reinforcement and deep reinforcement learning with real-world problems and implement any project that requires reinforcement and deep reinforcement learning knowledge from scratch. Audience This course is designed for beginners who know absolutely nothing about reinforcement and deep reinforcement learning, the ones who want to develop intelligent solutions, and the ones who want to learn the theoretical concepts first before implementing them using Python. An individual who wants to learn PySpark along with its implementation in realistic projects, machine learning or deep learning lovers, and anyone interested in artificial intelligence will be highly benefitted. You would need prior knowledge of Python, an elementary understanding of programming, and a willingness to learn and practice. | ||
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