[FreeTutorials.Us] [UDEMY] Cutting-Edge AI Deep Reinforcement Learning in Python [FTU]

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09/07/19 at 5:47pm GMT+1

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Description

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Apply deep learning to artificial intelligence and reinforcement learning using evolution strategies, A2C, and DDPG

HIGHEST RATED

Created by: Lazy Programmer Inc.

Last updated: 5/2019

Language: English

Caption (CC): Included

Torrent Contains: 102 Files, 7 Folders

Course Source: https://www.udemy.com/cutting-edge-artificial-intelligence/

What you'll learn

• Understand a cutting-edge implementation of the A2C algorithm (OpenAI Baselines)

• Understand and implement Evolution Strategies (ES) for AI

• Understand and implement DDPG (Deep Deterministic Policy Gradient)

Course content

all 48 lectures 08:23:48

Requirements

• Know the basics of MDPs (Markov Decision Processes) and Reinforcement Learning

• Helpful to have seen my first two Reinforcement Learning courses

• Know how to build a convolutional neural network in Tensorflow

Description

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!

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

• Know how to build a convolutional neural network (CNN) in TensorFlow

• Markov Decision Proccesses (MDPs)

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:

• Students and professionals who want to apply Reinforcement Learning to their work and projects

• Anyone who wants to learn cutting-edge Artificial Intelligence and Reinforcement Learning algorithms.

File list

[FreeTutorials.Us] [UDEMY] Cutting-Edge AI Deep Reinforcement Learning in Python [FTU]
0. Websites you may like/How you can help Team-FTU.txt 229 B
1. Welcome/1. Introduction.mp4 29.5 MB
1. Welcome/1. Introduction.vtt 4.4 KB
1. Welcome/2. Outline.mp4 54.3 MB
1. Welcome/2. Outline.vtt 9.3 KB
1. Welcome/3. Where to get the code.mp4 24.5 MB
1. Welcome/3. Where to get the code.vtt 5.6 KB
2. Review of Fundamental Reinforcement Learning Concepts/1. Review Section Introduction.mp4 18.9 MB
2. Review of Fundamental Reinforcement Learning Concepts/1. Review Section Introduction.vtt 4.6 KB
2. Review of Fundamental Reinforcement Learning Concepts/2. The Explore-Exploit Dilemma.mp4 71.6 MB
2. Review of Fundamental Reinforcement Learning Concepts/2. The Explore-Exploit Dilemma.vtt 15.5 KB
2. Review of Fundamental Reinforcement Learning Concepts/3. Markov Decision Processes (MDPs).mp4 108.7 MB
2. Review of Fundamental Reinforcement Learning Concepts/3. Markov Decision Processes (MDPs).vtt 22.3 KB
2. Review of Fundamental Reinforcement Learning Concepts/4. Monte Carlo Methods.mp4 32.1 MB
2. Review of Fundamental Reinforcement Learning Concepts/4. Monte Carlo Methods.vtt 8.5 KB
2. Review of Fundamental Reinforcement Learning Concepts/5. Temporal Difference Learning (TD).mp4 78.6 MB
2. Review of Fundamental Reinforcement Learning Concepts/5. Temporal Difference Learning (TD).vtt 18.6 KB
2. Review of Fundamental Reinforcement Learning Concepts/6. OpenAI Gym Warmup.mp4 49.7 MB
2. Review of Fundamental Reinforcement Learning Concepts/6. OpenAI Gym Warmup.vtt 7.2 KB
2. Review of Fundamental Reinforcement Learning Concepts/7. Review Section Summary.mp4 31.2 MB
2. Review of Fundamental Reinforcement Learning Concepts/7. Review Section Summary.vtt 8.3 KB
3. A2C (Advantage Actor-Critic)/1. A2C Section Introduction.mp4 61.3 MB
3. A2C (Advantage Actor-Critic)/1. A2C Section Introduction.vtt 9.2 KB
3. A2C (Advantage Actor-Critic)/10. A2C.mp4 192.3 MB
3. A2C (Advantage Actor-Critic)/10. A2C.vtt 19.7 KB
3. A2C (Advantage Actor-Critic)/11. A2C Section Summary.mp4 32.7 MB
3. A2C (Advantage Actor-Critic)/11. A2C Section Summary.vtt 7.8 KB
3. A2C (Advantage Actor-Critic)/2. A2C Theory (part 1).mp4 96.2 MB
3. A2C (Advantage Actor-Critic)/2. A2C Theory (part 1).vtt 22.8 KB
3. A2C (Advantage Actor-Critic)/3. A2C Theory (part 2).mp4 32.6 MB
3. A2C (Advantage Actor-Critic)/3. A2C Theory (part 2).vtt 7.9 KB
3. A2C (Advantage Actor-Critic)/4. A2C Theory (part 3).mp4 14.2 MB
3. A2C (Advantage Actor-Critic)/4. A2C Theory (part 3).vtt 3.4 KB
3. A2C (Advantage Actor-Critic)/5. A2C Demo.mp4 27.4 MB
3. A2C (Advantage Actor-Critic)/5. A2C Demo.vtt 2.4 KB
3. A2C (Advantage Actor-Critic)/6. A2C Code - Rough Sketch.mp4 28.5 MB
3. A2C (Advantage Actor-Critic)/6. A2C Code - Rough Sketch.vtt 8.1 KB
3. A2C (Advantage Actor-Critic)/7. Multiple Processes.mp4 70.1 MB
3. A2C (Advantage Actor-Critic)/7. Multiple Processes.vtt 9.6 KB
3. A2C (Advantage Actor-Critic)/8. Environment Wrappers.mp4 128.6 MB
3. A2C (Advantage Actor-Critic)/8. Environment Wrappers.vtt 13.3 KB
3. A2C (Advantage Actor-Critic)/9. Convolutional Neural Network.mp4 45.7 MB
3. A2C (Advantage Actor-Critic)/9. Convolutional Neural Network.vtt 6.1 KB
4. DDPG (Deep Deterministic Policy Gradient)/1. DDPG Section Introduction.mp4 23.9 MB
4. DDPG (Deep Deterministic Policy Gradient)/1. DDPG Section Introduction.vtt 3.9 KB
4. DDPG (Deep Deterministic Policy Gradient)/2. Deep Q-Learning (DQN) Review.mp4 45.2 MB
4. DDPG (Deep Deterministic Policy Gradient)/2. Deep Q-Learning (DQN) Review.vtt 10.5 KB
4. DDPG (Deep Deterministic Policy Gradient)/3. DDPG Theory.mp4 80.7 MB
4. DDPG (Deep Deterministic Policy Gradient)/3. DDPG Theory.vtt 20 KB
4. DDPG (Deep Deterministic Policy Gradient)/4. MuJoCo.mp4 110.5 MB
4. DDPG (Deep Deterministic Policy Gradient)/4. MuJoCo.vtt 21.1 KB
4. DDPG (Deep Deterministic Policy Gradient)/5. DDPG Code (part 1).mp4 193.6 MB
4. DDPG (Deep Deterministic Policy Gradient)/5. DDPG Code (part 1).vtt 20 KB
4. DDPG (Deep Deterministic Policy Gradient)/6. DDPG Code (part 2).mp4 64.8 MB
4. DDPG (Deep Deterministic Policy Gradient)/6. DDPG Code (part 2).vtt 6.2 KB
4. DDPG (Deep Deterministic Policy Gradient)/7. DDPG Section Summary.mp4 17.6 MB
4. DDPG (Deep Deterministic Policy Gradient)/7. DDPG Section Summary.vtt 4.6 KB
5. ES (Evolution Strategies)/1. ES Section Introduction.mp4 44.9 MB
5. ES (Evolution Strategies)/1. ES Section Introduction.vtt 7.5 KB
5. ES (Evolution Strategies)/2. ES Theory.mp4 108.2 MB
5. ES (Evolution Strategies)/2. ES Theory.vtt 22.4 KB
5. ES (Evolution Strategies)/3. Notes on Evolution Strategies.mp4 53.1 MB
5. ES (Evolution Strategies)/3. Notes on Evolution Strategies.vtt 10.1 KB
5. ES (Evolution Strategies)/4. ES for Optimizing a Function.mp4 46.5 MB
5. ES (Evolution Strategies)/4. ES for Optimizing a Function.vtt 6.8 KB
5. ES (Evolution Strategies)/5. ES for Supervised Learning.mp4 55.2 MB
5. ES (Evolution Strategies)/5. ES for Supervised Learning.vtt 6.7 KB
5. ES (Evolution Strategies)/6. Flappy Bird.mp4 60.9 MB
5. ES (Evolution Strategies)/6. Flappy Bird.vtt 13.8 KB
5. ES (Evolution Strategies)/7. ES for Flappy Bird in Code.mp4 142.2 MB
5. ES (Evolution Strategies)/7. ES for Flappy Bird in Code.vtt 15.5 KB
5. ES (Evolution Strategies)/8. ES for MuJoCo in Code.mp4 68.6 MB
5. ES (Evolution Strategies)/8. ES for MuJoCo in Code.vtt 8.1 KB
5. ES (Evolution Strategies)/9. ES Section Summary.mp4 28.6 MB
5. ES (Evolution Strategies)/9. ES Section Summary.vtt 5.7 KB
6. Appendix FAQ/1. What is the Appendix.mp4 18.1 MB
6. Appendix FAQ/1. What is the Appendix.vtt 3.3 KB
6. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 99.4 MB
6. Appendix FAQ/10. What order should I take your courses in (part 1).vtt 14.2 KB
6. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 139.4 MB
6. Appendix FAQ/11. What order should I take your courses in (part 2).vtt 20.2 KB
6. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4 194.3 MB
6. Appendix FAQ/2. Windows-Focused Environment Setup 2018.vtt 17.3 KB
6. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 167 MB
6. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.6 KB
6. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 117.5 MB
6. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 27.7 KB
6. Appendix FAQ/5. How to Succeed in this Course (Long Version).mp4 39.3 MB
6. Appendix FAQ/5. How to Succeed in this Course (Long Version).vtt 12.8 KB
6. Appendix FAQ/6. How to Code by Yourself (part 1).mp4 82.6 MB
6. Appendix FAQ/6. How to Code by Yourself (part 1).vtt 19.4 KB
6. Appendix FAQ/7. How to Code by Yourself (part 2).mp4 56.7 MB
6. Appendix FAQ/7. How to Code by Yourself (part 2).vtt 11.4 KB
6. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.mp4 78.3 MB

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