Practical Multi-Armed Bandit Algorithms in Python

描述

这门课程是您进入强化学习激动人心领域的完美切入点，在该领域中构建了数字人工智能代理以自动学习如何通过反复试验来做出顺序决策。具体而言，本课程着重于多武装强盗问题和各种算法策略的实际动手实现，以平衡勘探与开发之间的关系。每当您希望随着时间的推移不断从有限的选项中做出最佳选择时，您就会遇到一个多武装强盗问题，本课程将教您需要了解的每一个细节，以便能够建立切合实际的业务代理来处理这样的情况。

通过非常简洁的说明，本课程将教您如何轻松地将看似令人恐惧的数学公式自信地转换为Python代码。我们知道，我们中没有多少人精通数学，因此除非有必要，否则本课程有意远离数学。即使有必要谈论数学，本课程中采用的方法也使得具有基本代数技能的任何人都可以理解，最重要的是，可以轻松地将数学转换为代码并在此过程中建立有用的直觉。

本课程中讲授的一些算法策略是Epsilon Greedy，Softmax Exploration，乐观初始化，上限置信区间和Thompson采样。有了这些工具，您就有足够的能力来轻松构建和部署可处理不确定性下的关键业务运营的AI代理。

MP4 | 视频：h264，1280×720 | 音频：AAC，44.1 KHz，2声道
类型：在线学习| 语言：英语+ srt | 时长：13堂课（3h 45m）| 大小：1.15 GB
具有构建数字AI代理的技能，该代理能够在不确定性下自适应地做出关键业务决策。

您将学到的知识：

了解并能够识别多臂强盗问题。
将真实的业务问题建模为MAB，并实现数字AI代理以使其自动化。
了解RL在勘探开发难题方面的挑战。
各种算法策略的实际实现，以实现勘探与开发之间的平衡。
Epsilon-greedy策略的Python实现。
Softmax探索策略的Python实现。
乐观初始化策略的Python实现。
高可信度边界（UCB）策略的Python实现。
从奖励功能设计和样本效率方面了解RL的挑战。
通过增量采样来估算动作值。

要求

能够理解Python中的基本OOP程序。
具备Numpy和Matplotlib的基本知识。
基本的代数技能。如果您知道如何将数字相加，相减，相乘和相除，那您就可以了。

本课程的目标读者是那些

希望掌握Reinforcement Learning入门的具有Python基本技能的人。
想要将强化学习应用于实际业务问题的经验丰富的AI工程师，ML工程师，数据科学家和软件工程师。
愿意学习强化学习如何帮助自动化适应性决策流程的业务专业人士。

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 13 lectures (3h 45m) | Size: 1.15 GB

Acquire skills to build digital AI agents capable of adaptively making critical business decisions under uncertainties.

What you’ll learn:

• Understanding and being able to identify Multi-Armed Bandit problems.
• Modelling real business problems as MAB and implementing digital AI agents to automate them.
• Understanding the challenge of RL regarding the exploration-exploitation dilema.
• Practical implementation of the various algorithmic strategies for balancing between exploration and exploitation.
• Python implementation of the Epsilon-greedy strategy.
• Python implementation of the Softmax Exploration strategy.
• Python implementation of the Optimistic Initialization strategy.
• Python implementation of the Upper Confidence Bounds (UCB) strategy.
• Understand the challenges of RL in terms of the design of reward functions and sample efficiency.
• Estimation of action values through incremental sampling.

Requirements

• Be able to understand basic OOP programs in Python.
• Have basic Numpy and Matplotlib knowledge.
• Basic algebra skills. If you know how to add, subtract, multiply, and divide numbers, you are good to go.

Description

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.

Who this course is for

• Anyone with a basic Python skills desiring to the started in Reinforcement Learning.
• Experienced AI Engineers, ML Engineers, Data Scientist, and Software Engineers wanting to apply Reinforcement Learning to real business problems.
• Business professionals willing to learn how Reinforcement Learning can help with automating adaptive decision making processes.