Mô tả

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:

  • CartPole

  • Mountain Car

  • Atari games

To 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 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:

  • College-level math is helpful (calculus, probability)

  • Object-oriented programming

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

  • Numpy coding: matrix and vector operations

  • Linear regression

  • Gradient descent

  • Know how to build ANNs and CNNs in Theano or TensorFlow

  • Markov Decision Proccesses (MDPs)

  • Know how to implement Dynamic Programming, Monte Carlo, and Temporal Difference Learning to solve 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 FEATURES

  • Every line of code explained in detail - email me any time if you disagree

  • No 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 scratch

  • Not afraid of university-level math - get important details about algorithms that other courses leave out

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Nội dung khoá học

12 sections

Introduction and Logistics

4 lectures
Introduction and Outline
07:23
Where to get the Code
12:03
How to Succeed in this Course
03:04
Tensorflow or Theano - Your Choice!
04:09

The Basics of Reinforcement Learning

13 lectures
Reinforcement Learning Section Introduction
06:34
Elements of a Reinforcement Learning Problem
20:18
States, Actions, Rewards, Policies
09:24
Markov Decision Processes (MDPs)
10:07
The Return
04:56
Value Functions and the Bellman Equation
09:53
What does it mean to “learn”?
07:18
Solving the Bellman Equation with Reinforcement Learning (pt 1)
09:49
Solving the Bellman Equation with Reinforcement Learning (pt 2)
12:04
Epsilon-Greedy
06:09
Q-Learning
14:15
How to Learn Reinforcement Learning
05:56
Suggestion Box
03:10

OpenAI Gym and Basic Reinforcement Learning Techniques

13 lectures
OpenAI Gym Tutorial
05:43
Random Search
05:48
Saving a Video
02:18
CartPole with Bins (Theory)
03:51
CartPole with Bins (Code)
06:25
RBF Neural Networks
10:26
RBF Networks with Mountain Car (Code)
05:28
RBF Networks with CartPole (Theory)
01:54
RBF Networks with CartPole (Code)
03:11
Theano Warmup
03:04
Tensorflow Warmup
02:25
Plugging in a Neural Network
03:39
OpenAI Gym Section Summary
03:28

TD Lambda

5 lectures
N-Step Methods
03:14
N-Step in Code
03:40
TD Lambda
07:36
TD Lambda in Code
03:00
TD Lambda Summary
02:21

Policy Gradients

10 lectures
Policy Gradient Methods
11:38
Policy Gradient in TensorFlow for CartPole
07:19
Policy Gradient in Theano for CartPole
04:14
Continuous Action Spaces
04:16
Mountain Car Continuous Specifics
04:12
Mountain Car Continuous Theano
07:31
Mountain Car Continuous Tensorflow
08:07
Mountain Car Continuous Tensorflow (v2)
06:11
Mountain Car Continuous Theano (v2)
07:31
Policy Gradient Section Summary
01:36

Deep Q-Learning

10 lectures
Deep Q-Learning Intro
03:52
Deep Q-Learning Techniques
09:13
Deep Q-Learning in Tensorflow for CartPole
05:09
Deep Q-Learning in Theano for CartPole
04:48
Additional Implementation Details for Atari
05:36
Pseudocode and Replay Memory
06:15
Deep Q-Learning in Tensorflow for Breakout
23:47
Deep Q-Learning in Theano for Breakout
23:54
Partially Observable MDPs
04:52
Deep Q-Learning Section Summary
04:45

A3C

7 lectures
A3C - Theory and Outline
16:30
A3C - Code pt 1 (Warmup)
06:28
A3C - Code pt 2
06:27
A3C - Code pt 3
07:35
A3C - Code pt 4
18:02
A3C - Section Summary
02:05
Course Summary
04:57

Theano and Tensorflow Basics Review

4 lectures
(Review) Theano Basics
07:47
(Review) Theano Neural Network in Code
09:17
(Review) Tensorflow Basics
07:27
(Review) Tensorflow Neural Network in Code
09:43

Setting Up Your Environment (FAQ by Student Request)

3 lectures
Pre-Installation Check
04:12
Anaconda Environment Setup
20:20
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
17:32

Extra Help With Python Coding for Beginners (FAQ by Student Request)

5 lectures
How to Code by Yourself (part 1)
15:54
How to Code by Yourself (part 2)
09:23
Proof that using Jupyter Notebook is the same as not using it
12:29
Python 2 vs Python 3
04:38
Is Theano Dead?
10:03

Effective Learning Strategies for Machine Learning (FAQ by Student Request)

4 lectures
How to Succeed in this Course (Long Version)
10:24
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:04
Machine Learning and AI Prerequisite Roadmap (pt 1)
11:18
Machine Learning and AI Prerequisite Roadmap (pt 2)
16:07

Appendix / FAQ Finale

2 lectures
What is the Appendix?
02:48
BONUS
05:48

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