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Udemy - Advanced AI: Deep Reinforcement Learning in Python

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Udemy - Advanced AI: Deep Reinforcement Learning in Python (Size: 2.8 GB)
  1. (Review) Theano Basics.mp4 78.1 MB
  1. (Review) Theano Basics.srt 7.3 KB
  1. A3C - Theory and Outline.mp4 71.8 MB
  1. A3C - Theory and Outline.srt 20.3 KB
  1. Anaconda Environment Setup.mp4 186.2 MB
  1. Anaconda Environment Setup.srt 20.1 KB
  1. Deep Q-Learning Intro.mp4 5.9 MB
  1. Deep Q-Learning Intro.srt 4.8 KB
  1. How to Code by Yourself (part 1).mp4 24.5 MB
  1. How to Code by Yourself (part 1).srt 22.8 KB
  1. How to Succeed in this Course (Long Version).mp4 18.3 MB
  1. How to Succeed in this Course (Long Version).srt 14.5 KB
  1. Introduction and Outline.mp4 50.5 MB
  1. Introduction and Outline.srt 11.3 KB
  1. N-Step Methods.mp4 15.6 MB
  1. N-Step Methods.srt 3.8 KB
  1. OpenAI Gym Tutorial.mp4 8.7 MB
  1. OpenAI Gym Tutorial.srt 7.7 KB
  1. Policy Gradient Methods.mp4 17.9 MB
  1. Policy Gradient Methods.srt 14.8 KB
  1. Reinforcement Learning Section Introduction.mp4 41 MB
  1. Reinforcement Learning Section Introduction.srt 8.8 KB
  1. What is the Appendix.mp4 5.5 MB
  1. What is the Appendix.srt 3.7 KB
  10. Deep Q-Learning Section Summary.mp4 10.4 MB
  10. Deep Q-Learning Section Summary.srt 6 KB
  10. Epsilon-Greedy.mp4 40.2 MB
  10. Epsilon-Greedy.srt 7.5 KB
  10. Policy Gradient Section Summary.mp4 3.3 MB
  10. Policy Gradient Section Summary.srt 1.9 KB
  10. Theano Warmup.mp4 5.8 MB
  10. Theano Warmup.srt 3.5 KB
  11. Q-Learning.mp4 67.1 MB
  11. Q-Learning.srt 19 KB
  11. Tensorflow Warmup.mp4 5.1 MB
  11. Tensorflow Warmup.srt 2.5 KB
  12. How to Learn Reinforcement Learning.mp4 40.6 MB
  12. How to Learn Reinforcement Learning.srt 7.8 KB
  12. Plugging in a Neural Network.mp4 5.9 MB
  12. Plugging in a Neural Network.srt 4.8 KB
  13. OpenAI Gym Section Summary.mp4 5.3 MB
  13. OpenAI Gym Section Summary.srt 4.2 KB
  13. Suggestion Box.mp4 16.1 MB
  13. Suggestion Box.srt 4.7 KB
  2. (Review) Theano Neural Network in Code.mp4 67.7 MB
  2. (Review) Theano Neural Network in Code.srt 3.9 KB
  2. A3C - Code pt 1 (Warmup).mp4 50.1 MB
  2. A3C - Code pt 1 (Warmup).srt 7.8 KB
  2. BONUS.mp4 37.9 MB
  2. BONUS.srt 7.9 KB
  2. Deep Q-Learning Techniques.mp4 14.4 MB
  2. Deep Q-Learning Techniques.srt 12.3 KB
  2. Elements of a Reinforcement Learning Problem.mp4 105.2 MB
  2. Elements of a Reinforcement Learning Problem.srt 27.1 KB
  2. How to Code by Yourself (part 2).mp4 14.8 MB
  2. How to Code by Yourself (part 2).srt 13.3 KB
  2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.9 MB
  2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14.5 KB
  2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 0 B
  2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 0 B
  2. N-Step in Code.mp4 9.5 MB
  2. N-Step in Code.srt 4.2 KB
  2. Policy Gradient in TensorFlow for CartPole.mp4 18 MB
  2. Policy Gradient in TensorFlow for CartPole.srt 8.7 KB
  2. Random Search.mp4 10.3 MB
  2. Random Search.srt 6.9 KB
  2. Where to get the Code.mp4 51.6 MB
  2. Where to get the Code.srt 13.2 KB
  2.1 Github Link.html 102.4 B
  3. (Review) Tensorflow Basics.mp4 63.4 MB
  3. (Review) Tensorflow Basics.srt 6 KB
  3. A3C - Code pt 2.mp4 57.6 MB
  3. A3C - Code pt 2.srt 8.3 KB
  3. Deep Q-Learning in Tensorflow for CartPole.mp4 15 MB
  3. Deep Q-Learning in Tensorflow for CartPole.srt 5.8 KB
  3. How to Succeed in this Course.mp4 43.8 MB
  3. How to Succeed in this Course.srt 8.3 KB
  3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 29.3 MB
  3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 16 KB
  3. Policy Gradient in Theano for CartPole.mp4 13.4 MB
  3. Policy Gradient in Theano for CartPole.srt 4.5 KB
  3. Proof that using Jupyter Notebook is the same as not using it.mp4 78.3 MB
  3. Proof that using Jupyter Notebook is the same as not using it.srt 14.1 KB
  3. Saving a Video.mp4 4.5 MB
  3. Saving a Video.srt 2.4 KB
  3. States, Actions, Rewards, Policies.mp4 44.5 MB
  3. States, Actions, Rewards, Policies.srt 11.7 KB
  3. TD Lambda.mp4 11.8 MB
  3. TD Lambda.srt 9.3 KB
  4. (Review) Tensorflow Neural Network in Code.mp4 78.4 MB
  4. (Review) Tensorflow Neural Network in Code.srt 6 KB
  4. A3C - Code pt 3.mp4 84.5 MB
  4. A3C - Code pt 3.srt 9 KB
  4. CartPole with Bins (Theory).mp4 6 MB
  4. CartPole with Bins (Theory).srt 5.2 KB
  4. Continuous Action Spaces.mp4 6.6 MB
  4. Continuous Action Spaces.srt 5.3 KB
  4. Deep Q-Learning in Theano for CartPole.mp4 13.8 MB
  4. Deep Q-Learning in Theano for CartPole.srt 5.4 KB
  4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 37.6 MB
  4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 23 KB
  4. Markov Decision Processes (MDPs).mp4 50.9 MB
  4. Markov Decision Processes (MDPs).srt 13.3 KB
  4. Python 2 vs Python 3.mp4 7.8 MB
  4. Python 2 vs Python 3.srt 6.1 KB
  4. TD Lambda in Code.mp4 7.6 MB
  4. TD Lambda in Code.srt 3.3 KB
  4. Tensorflow or Theano - Your Choice!.mp4 18.9 MB
  4. Tensorflow or Theano - Your Choice!.srt 5.4 KB
  5. A3C - Code pt 4.mp4 184.3 MB
  5. A3C - Code pt 4.srt 21.2 KB
  5. Additional Implementation Details for Atari.mp4 8.5 MB
  5. Additional Implementation Details for Atari.srt 7 KB
  5. CartPole with Bins (Code).mp4 14.7 MB
  5. CartPole with Bins (Code).srt 8 KB
  5. Is Theano Dead.mp4 17.8 MB
  5. Is Theano Dead.srt 12.9 KB
  5. Mountain Car Continuous Specifics.mp4 6.5 MB
  5. Mountain Car Continuous Specifics.srt 5 KB
  5. TD Lambda Summary.mp4 3.6 MB
  5. TD Lambda Summary.srt 3 KB
  5. The Return.mp4 23.8 MB
  5. The Return.srt 6.7 KB
  6. A3C - Section Summary.mp4 8.9 MB
  6. A3C - Section Summary.srt 2.6 KB
  6. Mountain Car Continuous Theano.mp4 19.1 MB
  6. Mountain Car Continuous Theano.srt 9.9 KB
  6. Pseudocode and Replay Memory.mp4 27.8 MB
  6. Pseudocode and Replay Memory.srt 7.8 KB
  6. RBF Neural Networks.mp4 16.5 MB
  6. RBF Neural Networks.srt 14.6 KB
  6. Value Functions and the Bellman Equation.mp4 48.1 MB
  6. Value Functions and the Bellman Equation.srt 12.8 KB
  7. Course Summary.mp4 9.4 MB
  7. Course Summary.srt 6 KB
  7. Deep Q-Learning in Tensorflow for Breakout.mp4 234.6 MB
  7. Deep Q-Learning in Tensorflow for Breakout.srt 28.2 KB
  7. Mountain Car Continuous Tensorflow.mp4 20.1 MB
  7. Mountain Car Continuous Tensorflow.srt 10.3 KB
  7. RBF Networks with Mountain Car (Code).mp4 13.8 MB
  7. RBF Networks with Mountain Car (Code).srt 6.4 KB
  7. What does it mean to “learn”.mp4 31.8 MB
  7. What does it mean to “learn”.srt 8.9 KB
  8. Deep Q-Learning in Theano for Breakout.mp4 233.7 MB
  8. Deep Q-Learning in Theano for Breakout.srt 28.1 KB
  8. Mountain Car Continuous Tensorflow (v2).mp4 18.8 MB
  8. Mountain Car Continuous Tensorflow (v2).srt 7.1 KB
  8. RBF Networks with CartPole (Theory).mp4 3.1 MB
  8. RBF Networks with CartPole (Theory).srt 2.4 KB
  8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 42.9 MB
  8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt 12.4 KB
  9. Mountain Car Continuous Theano (v2).mp4 22.2 MB
  9. Mountain Car Continuous Theano (v2).srt 8.3 KB
  9. Partially Observable MDPs.mp4 7.6 MB
  9. Partially Observable MDPs.srt 5.8 KB
  9. RBF Networks with CartPole (Code).mp4 8.9 MB
  9. RBF Networks with CartPole (Code).srt 3.6 KB
  9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 57.3 MB
  9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt 15.5 KB
  [Tutorialsplanet.NET].url 102.4 B
  ▲ 162 total files

Description


Udemy - Advanced AI: Deep Reinforcement Learning in Python

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.
For more Udemy Courses: https://tutorialsplanet.net

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