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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-reinforcement-learning-in-python/
课程评论：没有评论
课程名称：高级人工智能：Python中的深度强化学习 课程概述： 本课程深入探讨如何通过深度学习和神经网络的应用，掌握强化学习的基础知识。你是否曾好奇像OpenAI的ChatGPT和GPT-4等人工智能技术是如何运作的？在本课程中，我们将揭示这些开创性应用的背后原理。强化学习自70年代以来就存在，但结合深度学习的进展使其在推动人工智能发展方面达到了前所未有的水平，从AlphaGo战胜围棋世界冠军到自动驾驶汽车的进步，强化学习为我们展现了其巨大的潜力。 课程中将介绍强化学习的基本原则，及其与监督和无监督学习的不同之处，强调其目的性和与环境相互作用的特性。我们会利用OpenAI Gym这个平台，进行实际的强化学习代理训练，涵盖复杂的环境，例如CartPole、Mountain Car和Atari游戏。 课程还将教授一系列新技术，包括时间差学习（TD Lambda算法）、径向基函数网络（RBF网络）、策略梯度方法以及深度Q学习（DQN）和异步优势演员-评论家（A3C）。通过这些内容，你将系统地学习如何从零开始实现机器学习算法，掌握强化学习的核心理念和应用技巧。 建议前提条件： - 大学水平数学知识（微积分、概率） - 面向对象编程 - Python编程：条件语句、循环、列表、字典、集合 - Numpy编程：矩阵和向量操作 - 线性回归与梯度下降 - 理解并构建人工神经网络（ANN）和卷积神经网络（CNN） - 马尔可夫决策过程（MDP） - 实现动态规划、蒙特卡罗方法和时间差学习以解决MDP 本课程注重每一行代码的详细讲解，确保你不仅仅是完成代码，而是真正理解其背后的原理和实现。通过丰富的实践，你将获得深层次的学习体验，充分把握深度强化学习的前沿技术与实际应用。
Ever wondered how AI technologies like OpenAI ChatGPT and GPT-4 really work? In this course, you will learn the foundations of these groundbreaking applications.This course is all about the application of deep learning and neural networks to reinforcement learning.If you've taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI.Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level.Reinforcement learning has been around since the 70s but none of this has been possible until now.The world is changing at a very fast pace. The state of California is changing their regulations so that self-driving car companies can test their cars without a human in the car to supervise.We've seen that reinforcement learning is an entirely different kind of machine learning than supervised and unsupervised learning.Supervised and unsupervised machine learning algorithms are for analyzing and making predictions about data, whereas reinforcement learning is about training an agent to interact with an environment and maximize its reward.Unlike supervised and unsupervised learning algorithms, reinforcement learning agents have an impetus - they want to reach a goal.This is such a fascinating perspective, it can even make supervised / unsupervised machine learning and "data science" seem boring in hindsight. Why train a neural network to learn about the data in a database, when you can train a neural network to interact with the real-world?While deep reinforcement learning and AI has a lot of potential, it also carries with it huge risk.Bill Gates and Elon Musk have made public statements about some of the risks that AI poses to economic stability and even our existence.As we learned in my first reinforcement learning course, one of the main principles of training reinforcement learning agents is that there are unintended consequences when training an AI.AIs don't think like humans, and so they come up with novel and non-intuitive solutions to reach their goals, often in ways that surprise domain experts - humans who are the best at what they do.OpenAI is a non-profit founded by Elon Musk, Sam Altman (Y Combinator), and others, in order to ensure that AI progresses in a way that is beneficial, rather than harmful.Part of the motivation behind OpenAI is the existential risk that AI poses to humans. They believe that open collaboration is one of the keys to mitigating that risk.One of the great things about OpenAI is that they have a platform called the OpenAI Gym, which we'll be making heavy use of in this course.It allows anyone, anywhere in the world, to train their reinforcement learning agents in standard environments.In this course, we'll build upon what we did in the last course by working with more complex environments, specifically, those provided by the OpenAI Gym:CartPoleMountain CarAtari gamesTo train effective learning agents, we'll need new techniques.We'll extend our knowledge of temporal difference learning by looking at the TD Lambda algorithm, we'll look at a special type of neural network called the RBF network, we'll look at the policy gradient method, and we'll end the course by looking at Deep Q-Learning (DQN) and A3C (Asynchronous Advantage Actor-Critic).Thanks for reading, and I'll see you in class!"If you can't implement it, you don't understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...Suggested Prerequisites:College-level math is helpful (calculus, probability)Object-oriented programmingPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operationsLinear regressionGradient descentKnow how to build ANNs and CNNs in Theano or TensorFlowMarkov Decision Proccesses (MDPs)Know how to implement Dynamic Programming, Monte Carlo, and Temporal Difference Learning to solve MDPsWHAT ORDER SHOULD I TAKE YOUR COURSES IN?:Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math - get important details about algorithms that other courses leave out
