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所在平台: Udemy |
课程主页: https://www.udemy.com/course/contextual-bandit-problems-in-python/
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课程名称：Python中的上下文多臂赌博机问题 课程概述：欢迎参加我们的课程，我们将逐步引导您了解多臂赌博机问题和上下文赌博机问题。无需任何先前经验，我们将从基础开始，逐步提升您的技能，使您能够将这些算法应用于自己的项目中。课程内容包括随机、贪婪、ε-贪婪和软最大等基本算法，以及更高级的方法，如上置信界（UCB）。我们将解释遗憾概念，不仅仅关注强化学习和多臂赌博机问题中的奖励值。 通过在确定性、随机和非平稳环境中的实际案例，您将看到这些算法的实际表现。课程还将探讨多臂赌博机问题与强化学习的关系，分析它们的相似点与不同点。 此外，我们还将深入讨论贝叶斯推断，介绍汤普森采样，包括二元奖励和实际值奖励的简单解释，并使用Beta和高斯分布来估计概率分布，配有清晰的示例以帮助您理解理论并实践应用。 我们将探索上下文赌博机问题，以LinUCB算法为指导，从基本的玩具示例到真实世界数据，您将学习如何运作并与较简单的方法（如ε-贪婪）进行比较。 无论您对Python是否熟悉，我们都有帮助您入门的内容。为了确保您真正掌握知识，课程中还包含一些测验来测试您的理解。我们的讲解清晰，代码整洁，并且增加了有趣的可视化，以帮助您更好地理解所有内容。加入我们，成为多臂和上下文赌博机问题的高手吧！
Welcome to our course where we'll guide you through Multi-armed Bandit Problems and Contextual Bandit Problems, step by step. No prior experience needed - we'll start from scratch and build up your skills so you can use these algorithms for your own projects.We'll cover the basics like random, greedy, e-greedy, softmax, and more advanced methods like Upper Confidence Bound (UCB). Along the way, we'll explain concepts like Regret concept instead of just focusing on rewards value in Reinforcement Learning and Multi-armed Bandit Problems. Through practical examples in different types of environments, like deterministic, stochastic and non-stationary environment, you'll see how these algorithms perform in action.Ever wondered how Multi-armed Bandit problems relate to Reinforcement Learning? We'll break it down for you, highlighting what's similar and what's different.We'll also dive into Bayesian inference, introducing you to Thompson sampling, both for binary reward and real value reward in simple terms, and use Beta and Gaussian distributions to estimate the probability distributions with clear examples to help you understand the theory and how to put it into practice.Then, we'll explore Contextual Bandit problems, using the LinUCB algorithm as our guide. From basic toy examples to real-world data, you'll see how it works and compare it to simpler methods like e-greedy.Don't worry if you're new to Python - we've got you covered with a section to help you get started. And to make sure you're really getting it, we'll throw in some quizzes to test your understanding along the way.Our explanations are clear, our code is clean, and we've added fun visualizations to help everything make sense. So join us on this journey and become a master of Multi-armed and Contextual Bandit Problems!
