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Dettagli torrent per "Udemy | Cutting-Edge AI Deep Reinforcement Learning in Python [FTU]"

Udemy | Cutting-Edge AI Deep Reinforcement Learning in Python [FTU]

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Nome:	Udemy \| Cutting-Edge AI Deep Reinforcement Learning in Python [FTU]
Descrizione: 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.
YouTube Video:
Categoria:	Tutorials
Lingua:	English
Dimensione totale:	3.31 GB
Info Hash:	9D445AAF0FFF5FFE5C45CAAFD66E692781FAF725
Aggiunto di:	Prom3th3uS
Data di aggiunta:	2019-09-07 04:23:31
Stato torrent:	Torrent Verified

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File List:

File	Dimensione
0. Websites you may like/0. (1Hack.Us) Premium Tutorials-Guides-Articles & Community based Forum.url	377.00 B
0. Websites you may like/1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url	328.00 B
0. Websites you may like/2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url	286.00 B
0. Websites you may like/3. (NulledPremium.com) Download E-Learning, E-Books, Audio-Books, Comics, Articles and more... etc.url	163.00 B
0. Websites you may like/4. (FTUApps.com) Download Cracked Developers Applications For Free.url	239.00 B
0. Websites you may like/How you can help Team-FTU.txt	229.00 B
1. Welcome/1. Introduction.mp4	29.55 MB
1. Welcome/1. Introduction.vtt	4.43 kB
1. Welcome/2. Outline.mp4	54.25 MB
1. Welcome/2. Outline.vtt	9.31 kB
1. Welcome/3. Where to get the code.mp4	24.45 MB
1. Welcome/3. Where to get the code.vtt	5.58 kB
2. Review of Fundamental Reinforcement Learning Concepts/1. Review Section Introduction.mp4	18.88 MB
2. Review of Fundamental Reinforcement Learning Concepts/1. Review Section Introduction.vtt	4.57 kB
2. Review of Fundamental Reinforcement Learning Concepts/2. The Explore-Exploit Dilemma.mp4	71.63 MB
2. Review of Fundamental Reinforcement Learning Concepts/2. The Explore-Exploit Dilemma.vtt	15.53 kB
2. Review of Fundamental Reinforcement Learning Concepts/3. Markov Decision Processes (MDPs).mp4	108.66 MB
2. Review of Fundamental Reinforcement Learning Concepts/3. Markov Decision Processes (MDPs).vtt	22.28 kB
2. Review of Fundamental Reinforcement Learning Concepts/4. Monte Carlo Methods.mp4	32.07 MB
2. Review of Fundamental Reinforcement Learning Concepts/4. Monte Carlo Methods.vtt	8.50 kB
2. Review of Fundamental Reinforcement Learning Concepts/5. Temporal Difference Learning (TD).mp4	78.57 MB
2. Review of Fundamental Reinforcement Learning Concepts/5. Temporal Difference Learning (TD).vtt	18.56 kB
2. Review of Fundamental Reinforcement Learning Concepts/6. OpenAI Gym Warmup.mp4	49.72 MB
2. Review of Fundamental Reinforcement Learning Concepts/6. OpenAI Gym Warmup.vtt	7.24 kB
2. Review of Fundamental Reinforcement Learning Concepts/7. Review Section Summary.mp4	31.17 MB
2. Review of Fundamental Reinforcement Learning Concepts/7. Review Section Summary.vtt	8.34 kB
3. A2C (Advantage Actor-Critic)/1. A2C Section Introduction.mp4	61.30 MB
3. A2C (Advantage Actor-Critic)/1. A2C Section Introduction.vtt	9.21 kB
3. A2C (Advantage Actor-Critic)/10. A2C.mp4	192.28 MB
3. A2C (Advantage Actor-Critic)/10. A2C.vtt	19.74 kB
3. A2C (Advantage Actor-Critic)/11. A2C Section Summary.mp4	32.72 MB
3. A2C (Advantage Actor-Critic)/11. A2C Section Summary.vtt	7.80 kB
3. A2C (Advantage Actor-Critic)/2. A2C Theory (part 1).mp4	96.21 MB
3. A2C (Advantage Actor-Critic)/2. A2C Theory (part 1).vtt	22.80 kB
3. A2C (Advantage Actor-Critic)/3. A2C Theory (part 2).mp4	32.59 MB
3. A2C (Advantage Actor-Critic)/3. A2C Theory (part 2).vtt	7.95 kB
3. A2C (Advantage Actor-Critic)/4. A2C Theory (part 3).mp4	14.22 MB
3. A2C (Advantage Actor-Critic)/4. A2C Theory (part 3).vtt	3.41 kB
3. A2C (Advantage Actor-Critic)/5. A2C Demo.mp4	27.42 MB
3. A2C (Advantage Actor-Critic)/5. A2C Demo.vtt	2.45 kB
3. A2C (Advantage Actor-Critic)/6. A2C Code - Rough Sketch.mp4	28.49 MB
3. A2C (Advantage Actor-Critic)/6. A2C Code - Rough Sketch.vtt	8.12 kB
3. A2C (Advantage Actor-Critic)/7. Multiple Processes.mp4	70.09 MB
3. A2C (Advantage Actor-Critic)/7. Multiple Processes.vtt	9.58 kB
3. A2C (Advantage Actor-Critic)/8. Environment Wrappers.mp4	128.58 MB
3. A2C (Advantage Actor-Critic)/8. Environment Wrappers.vtt	13.31 kB
3. A2C (Advantage Actor-Critic)/9. Convolutional Neural Network.mp4	45.66 MB
3. A2C (Advantage Actor-Critic)/9. Convolutional Neural Network.vtt	6.07 kB
4. DDPG (Deep Deterministic Policy Gradient)/1. DDPG Section Introduction.mp4	23.92 MB
4. DDPG (Deep Deterministic Policy Gradient)/1. DDPG Section Introduction.vtt	3.89 kB
4. DDPG (Deep Deterministic Policy Gradient)/2. Deep Q-Learning (DQN) Review.mp4	45.16 MB
4. DDPG (Deep Deterministic Policy Gradient)/2. Deep Q-Learning (DQN) Review.vtt	10.48 kB
4. DDPG (Deep Deterministic Policy Gradient)/3. DDPG Theory.mp4	80.68 MB
4. DDPG (Deep Deterministic Policy Gradient)/3. DDPG Theory.vtt	20.00 kB
4. DDPG (Deep Deterministic Policy Gradient)/4. MuJoCo.mp4	110.45 MB
4. DDPG (Deep Deterministic Policy Gradient)/4. MuJoCo.vtt	21.08 kB
4. DDPG (Deep Deterministic Policy Gradient)/5. DDPG Code (part 1).mp4	193.58 MB
4. DDPG (Deep Deterministic Policy Gradient)/5. DDPG Code (part 1).vtt	19.97 kB
4. DDPG (Deep Deterministic Policy Gradient)/6. DDPG Code (part 2).mp4	64.82 MB
4. DDPG (Deep Deterministic Policy Gradient)/6. DDPG Code (part 2).vtt	6.18 kB
4. DDPG (Deep Deterministic Policy Gradient)/7. DDPG Section Summary.mp4	17.60 MB
4. DDPG (Deep Deterministic Policy Gradient)/7. DDPG Section Summary.vtt	4.65 kB
5. ES (Evolution Strategies)/1. ES Section Introduction.mp4	44.86 MB
5. ES (Evolution Strategies)/1. ES Section Introduction.vtt	7.54 kB
5. ES (Evolution Strategies)/2. ES Theory.mp4	108.21 MB
5. ES (Evolution Strategies)/2. ES Theory.vtt	22.38 kB
5. ES (Evolution Strategies)/3. Notes on Evolution Strategies.mp4	53.10 MB
5. ES (Evolution Strategies)/3. Notes on Evolution Strategies.vtt	10.10 kB
5. ES (Evolution Strategies)/4. ES for Optimizing a Function.mp4	46.51 MB
5. ES (Evolution Strategies)/4. ES for Optimizing a Function.vtt	6.75 kB
5. ES (Evolution Strategies)/5. ES for Supervised Learning.mp4	55.16 MB
5. ES (Evolution Strategies)/5. ES for Supervised Learning.vtt	6.67 kB
5. ES (Evolution Strategies)/6. Flappy Bird.mp4	60.92 MB
5. ES (Evolution Strategies)/6. Flappy Bird.vtt	13.75 kB
5. ES (Evolution Strategies)/7. ES for Flappy Bird in Code.mp4	142.23 MB
5. ES (Evolution Strategies)/7. ES for Flappy Bird in Code.vtt	15.53 kB
5. ES (Evolution Strategies)/8. ES for MuJoCo in Code.mp4	68.63 MB
5. ES (Evolution Strategies)/8. ES for MuJoCo in Code.vtt	8.06 kB
5. ES (Evolution Strategies)/9. ES Section Summary.mp4	28.64 MB
5. ES (Evolution Strategies)/9. ES Section Summary.vtt	5.68 kB
6. Appendix FAQ/1. What is the Appendix.mp4	18.07 MB
6. Appendix FAQ/1. What is the Appendix.vtt	3.29 kB
6. Appendix FAQ/10. What order should I take your courses in (part 1).mp4	99.39 MB
6. Appendix FAQ/10. What order should I take your courses in (part 1).vtt	14.17 kB
6. Appendix FAQ/11. What order should I take your courses in (part 2).mp4	139.37 MB
6. Appendix FAQ/11. What order should I take your courses in (part 2).vtt	20.24 kB
6. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4	194.34 MB
6. Appendix FAQ/2. Windows-Focused Environment Setup 2018.vtt	17.33 kB
6. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4	167.01 MB
6. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt	12.59 kB
6. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4	117.54 MB
6. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt	27.68 kB
6. Appendix FAQ/5. How to Succeed in this Course (Long Version).mp4	39.26 MB
6. Appendix FAQ/5. How to Succeed in this Course (Long Version).vtt	12.83 kB
6. Appendix FAQ/6. How to Code by Yourself (part 1).mp4	82.57 MB
6. Appendix FAQ/6. How to Code by Yourself (part 1).vtt	19.38 kB
6. Appendix FAQ/7. How to Code by Yourself (part 2).mp4	56.70 MB
6. Appendix FAQ/7. How to Code by Yourself (part 2).vtt	11.44 kB
6. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.mp4	78.27 MB
6. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.vtt	12.31 kB
6. Appendix FAQ/9. Python 2 vs Python 3.mp4	18.98 MB
6. Appendix FAQ/9. Python 2 vs Python 3.vtt	5.35 kB

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Udemy | Cutting-Edge AI Deep Reinforcement Learning in Python [FTU]