Python中人工智能强化学习视频教程Artificial Intelligence: Reinforcement Learning in Python

当人们谈论人工智能时，他们通常不是指监督和无监督机器学习。

与我们认为的 AI 所做的事情相比，这些任务是相当微不足道的——下棋和围棋、驾驶汽车以及以超人的水平击败电子游戏。

强化学习最近因完成所有这些甚至更多而变得流行。

就像深度学习一样，很多理论都是在 70 年代和 80 年代发现的，但直到最近我们才能够亲眼观察到可能的惊人结果。

2016 年，我们看到谷歌的 AlphaGo 击败了围棋世界冠军。

我们看到人工智能在玩 Doom 和 Super Mario 等视频游戏。

自动驾驶汽车已经开始与其他司机甚至载客（优步）一起在真实的道路上行驶，而这一切都不需要人工协助。

如果这听起来很棒，那么请为未来做好准备，因为加速回报法则表明，这种进步只会继续呈指数级增长。

了解有监督和无监督机器学习是一项不小的壮举。迄今为止，仅针对这些主题，我就有超过二十五 (25！) 门课程。

然而，强化学习开辟了一个全新的世界。正如您将在本课程中学到的，强化学习范式非常来自监督学习和无监督学习。

它导致了行为心理学和神经科学方面的新的和惊人的见解。正如您将在本课程中学到的，在教授代理和教授动物甚至人类时，有许多类似的过程。这是迄今为止我们最接近真正的通用人工智能的东西。本课程涵盖哪些内容？

多臂强盗问题和探索-利用困境

计算均值和移动平均值的方法及其与随机梯度下降的关系

马尔可夫决策过程 (MDP)

动态规划

蒙特卡洛

时间差异 (TD) 学习（Q-Learning 和 SARSA）

近似方法（即如何将深度神经网络或其他可微模型插入您的 RL 算法）

如何使用 OpenAI Gym，零代码更改

项目：应用 Q-Learning 构建股票交易机器人

如果您准备好迎接全新的挑战，并了解您在传统监督机器学习、无监督机器学习甚至深度学习中从未见过的 AI 技术，那么本课程适合您。

MP4 | Video: h264, 1280×720 | Audio: AAC, 44100 Hz
Language: English | Size: 4.13 GB | Duration: 14h 38m

What you’ll learn
Apply gradient-based supervised machine learning methods to reinforcement learning
Understand reinforcement learning on a technical level
Understand the relationship between reinforcement learning and psychology
Implement 17 different reinforcement learning algorithms

Requirements
Calculus (derivatives)
Probability / Markov Models
Numpy, Matplotlib
Beneficial to have experience with at least a few supervised machine learning methods
Gradient descent
Good object-oriented programming skills
Description
When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning.

These tasks are pretty trivial compared to what we think of AIs doing – playing chess and Go, driving cars, and beating video games at a superhuman level.

Reinforcement learning has recently become popular for doing all of that and more.

Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn’t been until recently that we’ve been able to observe first hand the amazing results that are possible.

In 2016 we saw Google’s AlphaGo beat the world Champion in Go.

We saw AIs playing video games like Doom and Super Mario.

Self-driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance.

If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially.

Learning about supervised and unsupervised machine learning is no small feat. To date I have over TWENTY FIVE (25!) courses just on those topics alone.

And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is very from both supervised and unsupervised learning.

It’s led to new and amazing insights both in behavioral psychology and neuroscience. As you’ll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true artificial general intelligence. What’s covered in this course?

The multi-armed bandit problem and the explore-exploit dilemma

Ways to calculate means and moving averages and their relationship to stochastic gradient descent

Markov Decision Processes (MDPs)

Dynamic Programming

Monte Carlo

Temporal Difference (TD) Learning (Q-Learning and SARSA)

Approximation Methods (i.e. how to plug in a deep neural network or other differentiable model into your RL algorithm)

How to use OpenAI Gym, with zero code changes

Project: Apply Q-Learning to build a stock trading bot

If you’re ready to take on a brand new challenge, and learn about AI techniques that you’ve never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.

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 scratch

Other 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:

Calculus

Probability

Object-oriented programming

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations

Linear regression

Gradient descent

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)

Who this course is for:
Anyone who wants to learn about artificial intelligence, data science, machine learning, and deep learning
Both students and professionals