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所在平台: Udemy |
课程主页: https://www.udemy.com/course/reinforcement-learning-in-arabic-part-1/
课程评论：没有评论
课程名称：阿拉伯语强化学习（第一部分） 课程概述：强化学习（RL）是机器学习的一个领域，关注智能体应如何在环境中采取行动以最大化累积奖励的概念。在本课程中，您将学习强化学习的基本概念和算法，并开始实践应用。通过本教程，建立强化学习及其算法的坚实基础。本课程是两部分中的第一部分，主要包括强化学习的基本概念和思路，如Q学习和马尔可夫决策过程（MDP）。此外，我们还将学习基于深度学习的更现代化算法，如深度Q网络（DQN）。每个模块都包含概念插图以及实际实现的演练（源代码可用）。 本课程包含6节讲座（约2小时的教学内容），具体如下： - 引言（1节讲座） - 马尔可夫决策过程（2节讲座） - Q学习（1节讲座） - 深度Q网络（2节讲座） 先修知识： - 基础数学知识 - 基础Python知识 此课程源于一系列YouTube视频，现已有超过16K的观看次数和大量积极评价。现在它将搬到Udemy上，以便更好地进行内容管理和与学生互动。希望您觉得本课程有用，我们在第二部分见！
"Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward". In this course, you will learn the concepts and the algorithms behind and you will start to apply RL practically. Build a strong foundation in Reinforcement Learning and its algorithms with this tutorial.This course is the first of two parts. In this part, we will start with basic concepts and ideas that form the foundation of the RL. Concepts like: Q-Learning and Markov decision processes (MDPs). Then we will learn more recent algorithms (built on deep learning) like Deep Q-Networks. Each module contains a concept illustration together with a real implementation walkthrough (source code is available)The course consists of 6 lectures (~2 hours of material) described as follows:Introduction (1 lecture)Markov Decision Process (2 lectures)Q-Learning (1 lecture)Deep Q-Networks (2 lectures)PrerequisitesBasic Mathematics knowledgeBasic Python knowledgeThis course started as a youtube series that currently has 16K+ views and a lot of very positive comments. Now it will be available on Udemy for better content management and interaction with students.I hope you find the course useful and see you in Part 2!
