|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-multi-armed-bandit-algorithms-in-python/
课程评论：没有评论
**课程名称：Python实战多元臂老虎机算法** **课程概述：** 本课程是您进入强化学习领域的绝佳起点，学习如何构建人工智能代理，通过试错自动学习顺序决策。课程重点关注多元臂老虎机（Multi-Armed Bandit, MAB）问题，并教授如何在Python中实践各种算法策略，以平衡“探索”与“利用”。 当您需要长期在有限选项中做出最优选择时，您就面临着MAB问题。本课程将为您提供构建现实业务代理所需的全部知识。通过简洁明了的讲解，本课程将帮助您轻松地将看似复杂的数学公式转化为Python代码，即使数学基础不扎实也能理解。课程会尽量避免不必要的数学推导，即使涉及，也会采用基础代数知识即可理解的方式，并方便您将数学概念转化为代码，建立直观理解。 课程将教授多种算法策略，包括： * Epsilon Greedy（epsilon-贪婪算法） * Softmax Exploration（Softmax探索） * Optimistic Initialization（乐观初始化） * Upper Confidence Bounds（UCB，置信上界） * Thompson Sampling（汤普森采样） 掌握这些工具后，您将能够构建和部署人工智能代理，以应对不确定性下的关键业务运营。为弥合理论与实践的差距，本课程还包含在机器人技术（使用EV3 Mindstorm）中的应用案例，未来还将加入优化广告投放的应用部分。
This course is your perfect entry point into the exciting field of Reinforcement Learning where digital Artificial Intelligence agents are built to automatically learn how to make sequential decisions through trial-and-error. Specifically, this course focuses on the Multi-Armed Bandit problems and the practical hands-on implementation of various algorithmic strategies for balancing between exploration and exploitation. Whenever you desire to consistently make the best choice out of a limited number of options over time, you are dealing with a Multi-Armed Bandit problem and this course teaches you every detail you need to know to be able to build realistic business agents to handle such situations. With very concise explanations, this course teaches you how to confidently translate seemingly scary mathematical formulas into Python code painlessly. We understand that not many of us are technically adept in the subject of mathematics so this course intentionally stays away from maths unless it is necessary. And even when it becomes necessary to talk about mathematics, the approach taken in this course is such that anyone with basic algebra skills can understand and most importantly easily translate the maths into code and build useful intuitions in the process.Some of the algorithmic strategies taught in this course are Epsilon Greedy, Softmax Exploration, Optimistic Initialization, Upper Confidence Bounds, and Thompson Sampling. With these tools under your belt, you are adequately equipped to readily build and deploy AI agents that can handle critical business operations under uncertainties. To bridge the gap between theory and application, I've updated this course to include a section where I show how to apply the MAB algorithms in Robotics using the EV3 Mindstorm. I'll soon upload a section that will show how to apply the algorithms taught in this course to optimize advertisements.
