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所在平台: Udemy |
课程主页: https://www.udemy.com/course/cutting-edge-artificial-intelligence/
课程评论：没有评论
课程名称：前沿人工智能：Python中的深度强化学习 课程概述： 你是否曾想过像OpenAI的ChatGPT和GPT-4这些人工智能技术是如何运作的？在这门课程中，你将学习这些突破性应用的基础知识。欢迎来到前沿人工智能！这其实是我深度学习系列中的第11部分，也是我的第三门强化学习课程。深度强化学习结合了两个主题：强化学习和深度学习（神经网络）。尽管这两者均已存在相当长的时间，但深度学习的快速发展使得强化学习取得了重大进步。 近年来，深度学习的成熟推动了强化学习的发展，尽管其某些方面，如贝尔曼方程，早已存在于更久远的历史中。我们已看到AlphaZero在仅凭自我对弈的情况下掌握围棋，这只发生在AlphaGo击败世界围棋冠军仅仅几年之后。此外，真实世界中的机器人也学会了行走，甚至在被击倒后恢复，尽管它们仅在模拟中训练。 本课程将探讨如何改进这些算法，包括强大的A2C（优势演员-评论家）算法、DDPG（深度确定性策略梯度）算法，以及进化策略。进化策略是一种新的强化学习方法，它摒弃了旧有理论，采用了更"黑箱"的方法，受生物进化的启发。 我们将探索多种环境，包括经典的Atari游戏，这表明强化学习代理能够仅通过图像学习；MuJoCo物理模拟器，这是构建能理解物理的机器人迈出的第一步；以及大家熟知的Flappy Bird移动游戏。 我的课程中你将学习如何从头开始实现机器学习算法，而不仅仅是将数据插入库中。我会详细解释每一行代码，并欢迎你随时发邮件与我讨论。如果你想掌握真正的知识，学习如何在Python中解决深度强化学习的实际问题，这是一个理想的机会。 建议的先决条件包括：微积分、概率、面向对象编程、Python基础，以及对卷积神经网络（CNN）和马尔可夫决策过程（MDP）的了解。 欢迎参与课程，一起探索深度强化学习的前沿！
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.Welcome to Cutting-Edge AI!This is technically Deep Learning in Python part 11 of my deep learning series, and my 3rd reinforcement learning course.Deep Reinforcement Learning is actually the combination of 2 topics: Reinforcement Learning and Deep Learning (Neural Networks).While both of these have been around for quite some time, it's only been recently that Deep Learning has really taken off, and along with it, Reinforcement Learning.The maturation of deep learning has propelled advances in reinforcement learning, which has been around since the 1980s, although some aspects of it, such as the Bellman equation, have been for much longer.Recently, these advances have allowed us to showcase just how powerful reinforcement learning can be.We've seen how AlphaZero can master the game of Go using only self-play.This is just a few years after the original AlphaGo already beat a world champion in Go.We've seen real-world robots learn how to walk, and even recover after being kicked over, despite only being trained using simulation.Simulation is nice because it doesn't require actual hardware, which is expensive. If your agent falls down, no real damage is done.We've seen real-world robots learn hand dexterity, which is no small feat.Walking is one thing, but that involves coarse movements. Hand dexterity is complex - you have many degrees of freedom and many of the forces involved are extremely subtle.Imagine using your foot to do something you usually do with your hand, and you immediately understand why this would be difficult.Last but not least - video games.Even just considering the past few months, we've seen some amazing developments. AIs are now beating professional players in CS:GO and Dota 2.So what makes this course different from the first two?Now that we know deep learning works with reinforcement learning, the question becomes: how do we improve these algorithms?This course is going to show you a few different ways: including the powerful A2C (Advantage Actor-Critic) algorithm, the DDPG (Deep Deterministic Policy Gradient) algorithm, and evolution strategies.Evolution strategies is a new and fresh take on reinforcement learning, that kind of throws away all the old theory in favor of a more "black box" approach, inspired by biological evolution.What's also great about this new course is the variety of environments we get to look at.First, we're going to look at the classic Atari environments. These are important because they show that reinforcement learning agents can learn based on images alone.Second, we're going to look at MuJoCo, which is a physics simulator. This is the first step to building a robot that can navigate the real-world and understand physics - we first have to show it can work with simulated physics.Finally, we're going to look at Flappy Bird, everyone's favorite mobile game just a few years ago.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:CalculusProbabilityObject-oriented programmingPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operationsLinear regressionGradient descentKnow how to build a convolutional neural network (CNN) in TensorFlowMarkov Decision Proccesses (MDPs)WHAT 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
