Udemy - Artificial Intelligence: Reinforcement Learning in Python

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[GigaCourse.com] Udemy - Artificial Intelligence Reinforcement Learning in Python 1. Welcome
  • 1. Introduction.mp4 (34.2 MB)
  • 1. Introduction.srt (4.2 KB)
  • 2. Where to get the Code.mp4 (4.4 MB)
  • 2. Where to get the Code.srt (5.4 KB)
  • 3. Strategy for Passing the Course.mp4 (9.5 MB)
  • 3. Strategy for Passing the Course.srt (11.8 KB)
  • 4. Course Outline.mp4 (31.0 MB)
  • 4. Course Outline.srt (6.8 KB)
10. Stock Trading Project with Reinforcement Learning
  • 1. Stock Trading Project Section Introduction.mp4 (26.8 MB)
  • 1. Stock Trading Project Section Introduction.srt (6.8 KB)
  • 2. Data and Environment.mp4 (52.0 MB)
  • 2. Data and Environment.srt (15.7 KB)
  • 3. How to Model Q for Q-Learning.mp4 (44.9 MB)
  • 3. How to Model Q for Q-Learning.srt (12.0 KB)
  • 4. Design of the Program.mp4 (23.3 MB)
  • 4. Design of the Program.srt (8.5 KB)
  • 5. Code pt 1.mp4 (49.7 MB)
  • 5. Code pt 1.srt (9.6 KB)
  • 6. Code pt 2.mp4 (65.3 MB)
  • 6. Code pt 2.srt (11.8 KB)
  • 7. Code pt 3.mp4 (33.7 MB)
  • 7. Code pt 3.srt (5.4 KB)
  • 8. Code pt 4.mp4 (49.1 MB)
  • 8. Code pt 4.srt (8.0 KB)
  • 9. Stock Trading Project Discussion.mp4 (15.8 MB)
  • 9. Stock Trading Project Discussion.srt (4.3 KB)
11. Appendix FAQ
  • 1. What is the Appendix.mp4 (5.5 MB)
  • 1. What is the Appendix.srt (3.7 KB)
  • 10. What order should I take your courses in (part 1).mp4 (29.3 MB)
  • 10. What order should I take your courses in (part 1).srt (16.0 KB)
  • 11. What order should I take your courses in (part 2).mp4 (37.6 MB)
  • 11. What order should I take your courses in (part 2).srt (23.0 KB)
  • 12. BONUS Where to get discount coupons and FREE deep learning material.mp4 (37.8 MB)
  • 12. BONUS Where to get discount coupons and FREE deep learning material.srt (7.9 KB)
  • 2. Windows-Focused Environment Setup 2018.mp4 (186.4 MB)
  • 2. Windows-Focused Environment Setup 2018.srt (20.1 KB)
  • 3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 (43.9 MB)
  • 3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt (18.3 KB)
  • 4. How to Code by Yourself (part 1).mp4 (24.5 MB)
  • 4. How to Code by Yourself (part 1).srt (30.2 KB)
  • 5. How to Code by Yourself (part 2).mp4 (14.8 MB)
  • 5. How to Code by Yourself (part 2).srt (18.4 KB)
  • 6. How to Succeed in this Course (Long Version).mp4 (18.3 MB)
  • 6. How to Succeed in this Course (Long Version).srt (14.5 KB)
  • 7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 (39.0 MB)
  • 7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt (31.8 KB)
  • 8. Proof that using Jupyter Notebook is the same as not using it.mp4 (78.3 MB)
  • 8. Proof that using Jupyter Notebook is the same as not using it.srt (14.1 KB)
  • 9. Python 2 vs Python 3.mp4 (7.8 MB)
  • 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)
  • 1. Problem Setup and The Explore-Exploit Dilemma.srt (7.8 KB)
  • 10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4 (10.6 MB)
  • 10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.srt (6.1 KB)
  • 11. Nonstationary Bandits.mp4 (7.5 MB)
  • 11. Nonstationary Bandits.srt (7.8 KB)
  • 12. Bandit Summary, Real Data, and Online Learning.mp4 (33.9 MB)
  • 12. Bandit Summary, Real Data, and Online Learning.srt (9.1 KB)
  • 2. Applications of the Explore-Exploit Dilemma.mp4 (51.2 MB)
  • 2. Applications of the Explore-Exploit Dilemma.srt (10.9 KB)
  • 3. Epsilon-Greedy.mp4 (2.8 MB)
  • 3. Epsilon-Greedy.srt (3.2 KB)
  • 4. Updating a Sample Mean.mp4 (2.2 MB)
  • 4. Updating a Sample Mean.srt (2.2 KB)
  • 5. Designing Your Bandit Program.mp4 (24.5 MB)
  • 5. Designing Your Bandit Program.srt (5.6 KB)
  • 6. Comparing Different Epsilons.mp4 (8.0 MB)
  • 6. Comparing Different Epsilons.srt (5.3 KB)
  • 7. Optimistic Initial Values.mp4 (15.8 MB)
  • 7. Optimistic Initial Values.srt (3.1 KB)
  • 8. UCB1.mp4 (8.2 MB)
  • 8. UCB1.srt (8.1 KB)
  • 9. Bayesian Thompson Sampling.mp4 (51.8 MB)
  • 9. Bayesian Thompson Sampling.srt (11.8 KB)
3. High Level Overview of Reinforcement Learning
  • 1. What is Reinforcement Learning.mp4 (54.6 MB)
  • 1. What is Reinforcement Learning.srt (10.9 KB)
  • 2. On Unusual or Unexpected Strategies of RL.mp4 (37.1 MB)
  • 2. On Unusual or Unexpected Strategies of RL.srt (7.9 KB)
  • 3. Defining Some Terms.mp4 (42.3 MB)
  • 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)
  • 1. Naive Solution to Tic-Tac-Toe.srt (7.2 KB)
  • 10. Tic Tac Toe Code Main Loop and Demo.mp4 (9.4 MB)
  • 10. Tic Tac Toe Code Main Loop and Demo.srt (9.2 KB)
  • 11. Tic Tac Toe Summary.mp4 (8.3 MB)
  • 11. Tic Tac Toe Summary.srt (10.2 KB)
  • 12. Tic Tac Toe Exercise.mp4 (19.8 MB)
  • 12. Tic Tac Toe Exercise.srt (4.6 KB)
  • 2. Components of a Reinforcement Learning System.mp4 (12.7 MB)
  • 2. Components of a Reinforcement Learning System.srt (14.8 KB)
  • 3. Notes on Assigning Rewards.mp4 (4.2 MB)
  • 3. Notes on Assigning Rewards.srt (4.9 KB)
  • 4. The Value Function and Your First Reinforcement Learning Algorithm.mp4 (103.7 MB)
  • 4. The Value Function and Your First Reinforcement Learning Algorithm.srt (22.8 KB)
  • 5. Tic Tac Toe Code Outline.mp4 (5.0 MB)
  • 5. Tic Tac Toe Code Outline.srt (6.4 KB)
  • 6. Tic Tac Toe Code Representing States.mp4 (4.4 MB)
  • 6. Tic Tac Toe Code Representing States.srt (4.9 KB)
  • 7. Tic Tac Toe Code Enumerating States Recursively.mp4 (9.8 MB)
  • 7. Tic Tac Toe Code Enumerating States Recursively.srt (11.3 KB)
  • 8. Tic Tac Toe Code The Environment.mp4 (10.0 MB)

Description

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]



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1.9 GB
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Udemy - Artificial Intelligence: Reinforcement Learning in Python


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