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[GigaCourse.com] Udemy - Artificial Intelligence Reinforcement Learning in Python

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Udemy - Artificial Intelligence: Reinforcement Learning in Python

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 SIXTEEN (16!) 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 more different from supervised and unsupervised learning than they are from each other.

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 general artificial 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)
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!

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

TIPS (for getting through the course):

Watch it at 2x.
Take handwritten notes. This will drastically increase your ability to retain the information.
Write down the equations. If you don't, I guarantee it will just look like gibberish.
Ask lots of questions on the discussion board. The more the better!
Realize that most exercises will take you days or weeks to complete.
Write code yourself, don't just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "What order should I take your courses in?" (available in the Appendix 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

Created by Lazy Programmer Inc.
Last updated 1/2020
English
English [Auto-generated]

File list

[GigaCourse.com] Udemy - Artificial Intelligence Reinforcement Learning in Python
1. Welcome/1. Introduction.mp4 34.2 MB
1. Welcome/1. Introduction.srt 4.2 KB
1. Welcome/2. Where to get the Code.mp4 4.4 MB
1. Welcome/2. Where to get the Code.srt 5.4 KB
1. Welcome/3. Strategy for Passing the Course.mp4 9.5 MB
1. Welcome/3. Strategy for Passing the Course.srt 11.8 KB
1. Welcome/4. Course Outline.mp4 31 MB
1. Welcome/4. Course Outline.srt 6.8 KB
10. Stock Trading Project with Reinforcement Learning/1. Stock Trading Project Section Introduction.mp4 26.8 MB
10. Stock Trading Project with Reinforcement Learning/1. Stock Trading Project Section Introduction.srt 6.8 KB
10. Stock Trading Project with Reinforcement Learning/2. Data and Environment.mp4 52 MB
10. Stock Trading Project with Reinforcement Learning/2. Data and Environment.srt 15.7 KB
10. Stock Trading Project with Reinforcement Learning/3. How to Model Q for Q-Learning.mp4 44.9 MB
10. Stock Trading Project with Reinforcement Learning/3. How to Model Q for Q-Learning.srt 12 KB
10. Stock Trading Project with Reinforcement Learning/4. Design of the Program.mp4 23.3 MB
10. Stock Trading Project with Reinforcement Learning/4. Design of the Program.srt 8.5 KB
10. Stock Trading Project with Reinforcement Learning/5. Code pt 1.mp4 49.7 MB
10. Stock Trading Project with Reinforcement Learning/5. Code pt 1.srt 9.6 KB
10. Stock Trading Project with Reinforcement Learning/6. Code pt 2.mp4 65.3 MB
10. Stock Trading Project with Reinforcement Learning/6. Code pt 2.srt 11.8 KB
10. Stock Trading Project with Reinforcement Learning/7. Code pt 3.mp4 33.7 MB
10. Stock Trading Project with Reinforcement Learning/7. Code pt 3.srt 5.4 KB
10. Stock Trading Project with Reinforcement Learning/8. Code pt 4.mp4 49.1 MB
10. Stock Trading Project with Reinforcement Learning/8. Code pt 4.srt 8 KB
10. Stock Trading Project with Reinforcement Learning/9. Stock Trading Project Discussion.mp4 15.8 MB
10. Stock Trading Project with Reinforcement Learning/9. Stock Trading Project Discussion.srt 4.3 KB
11. Appendix FAQ/1. What is the Appendix.mp4 5.5 MB
11. Appendix FAQ/1. What is the Appendix.srt 3.7 KB
11. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 29.3 MB
11. Appendix FAQ/10. What order should I take your courses in (part 1).srt 16 KB
11. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 37.6 MB
11. Appendix FAQ/11. What order should I take your courses in (part 2).srt 23 KB
11. Appendix FAQ/12. BONUS Where to get discount coupons and FREE deep learning material.mp4 37.8 MB
11. Appendix FAQ/12. BONUS Where to get discount coupons and FREE deep learning material.srt 7.9 KB
11. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4 186.4 MB
11. Appendix FAQ/2. Windows-Focused Environment Setup 2018.srt 20.1 KB
11. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.9 MB
11. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 18.3 KB
11. Appendix FAQ/4. How to Code by Yourself (part 1).mp4 24.5 MB
11. Appendix FAQ/4. How to Code by Yourself (part 1).srt 30.2 KB
11. Appendix FAQ/5. How to Code by Yourself (part 2).mp4 14.8 MB
11. Appendix FAQ/5. How to Code by Yourself (part 2).srt 18.4 KB
11. Appendix FAQ/6. How to Succeed in this Course (Long Version).mp4 18.3 MB
11. Appendix FAQ/6. How to Succeed in this Course (Long Version).srt 14.5 KB
11. Appendix FAQ/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39 MB
11. Appendix FAQ/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 31.8 KB
11. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.mp4 78.3 MB
11. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.srt 14.1 KB
11. Appendix FAQ/9. Python 2 vs Python 3.mp4 7.8 MB
11. Appendix FAQ/9. Python 2 vs Python 3.srt 6.1 KB
2. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.mp4 6.5 MB
2. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.srt 7.8 KB
2. Return of the Multi-Armed Bandit/10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4 10.6 MB
2. Return of the Multi-Armed Bandit/10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.srt 6.1 KB
2. Return of the Multi-Armed Bandit/11. Nonstationary Bandits.mp4 7.5 MB
2. Return of the Multi-Armed Bandit/11. Nonstationary Bandits.srt 7.8 KB
2. Return of the Multi-Armed Bandit/12. Bandit Summary, Real Data, and Online Learning.mp4 33.9 MB
2. Return of the Multi-Armed Bandit/12. Bandit Summary, Real Data, and Online Learning.srt 9.1 KB
2. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.mp4 51.2 MB
2. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.srt 10.9 KB
2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy.mp4 2.8 MB
2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy.srt 3.2 KB
2. Return of the Multi-Armed Bandit/4. Updating a Sample Mean.mp4 2.2 MB
2. Return of the Multi-Armed Bandit/4. Updating a Sample Mean.srt 2.2 KB
2. Return of the Multi-Armed Bandit/5. Designing Your Bandit Program.mp4 24.5 MB
2. Return of the Multi-Armed Bandit/5. Designing Your Bandit Program.srt 5.6 KB
2. Return of the Multi-Armed Bandit/6. Comparing Different Epsilons.mp4 8 MB
2. Return of the Multi-Armed Bandit/6. Comparing Different Epsilons.srt 5.3 KB
2. Return of the Multi-Armed Bandit/7. Optimistic Initial Values.mp4 15.8 MB
2. Return of the Multi-Armed Bandit/7. Optimistic Initial Values.srt 3.1 KB
2. Return of the Multi-Armed Bandit/8. UCB1.mp4 8.2 MB
2. Return of the Multi-Armed Bandit/8. UCB1.srt 8.1 KB
2. Return of the Multi-Armed Bandit/9. Bayesian Thompson Sampling.mp4 51.8 MB
2. Return of the Multi-Armed Bandit/9. Bayesian Thompson Sampling.srt 11.8 KB
3. High Level Overview of Reinforcement Learning/1. What is Reinforcement Learning.mp4 54.6 MB
3. High Level Overview of Reinforcement Learning/1. What is Reinforcement Learning.srt 10.9 KB
3. High Level Overview of Reinforcement Learning/2. On Unusual or Unexpected Strategies of RL.mp4 37.1 MB
3. High Level Overview of Reinforcement Learning/2. On Unusual or Unexpected Strategies of RL.srt 7.9 KB
3. High Level Overview of Reinforcement Learning/3. Defining Some Terms.mp4 42.3 MB
3. High Level Overview of Reinforcement Learning/3. Defining Some Terms.srt 9.1 KB
4. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.mp4 6.1 MB
4. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.srt 7.2 KB
4. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.mp4 9.4 MB
4. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.srt 9.2 KB
4. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.mp4 8.3 MB
4. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.srt 10.2 KB
4. Build an Intelligent Tic-Tac-Toe Agent/12. Tic Tac Toe Exercise.mp4 19.8 MB
4. Build an Intelligent Tic-Tac-Toe Agent/12. Tic Tac Toe Exercise.srt 4.6 KB
4. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.mp4 12.7 MB
4. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.srt 14.8 KB
4. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.mp4 4.2 MB
4. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.srt 4.9 KB
4. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.mp4 103.7 MB
4. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.srt 22.8 KB
4. Build an Intelligent Tic-Tac-Toe Agent/5. Tic Tac Toe Code Outline.mp4 5 MB
4. Build an Intelligent Tic-Tac-Toe Agent/5. Tic Tac Toe Code Outline.srt 6.4 KB
4. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.mp4 4.4 MB
4. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.srt 4.9 KB
4. Build an Intelligent Tic-Tac-Toe Agent/7. Tic Tac Toe Code Enumerating States Recursively.mp4 9.8 MB

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