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Udemy Advanced AI Deep Reinforcement Learning in Python

Torrent Info
Category Other
Added Tuesday 24th October 2023 05:18:27 PM (3 years ago)
Size2.8 GB
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Torrent Files (162)
FilenameSize
1. Introduction and Outline.mp450.5 MB
1. Introduction and Outline.srt11.3 KB
2. Where to get the Code.mp451.6 MB
2. Where to get the Code.srt13.2 KB
2.1 Github Link.html120 bytes
3. How to Succeed in this Course.mp443.8 MB
3. How to Succeed in this Course.srt8.3 KB
4. Tensorflow or Theano - Your Choice!.mp418.9 MB
4. Tensorflow or Theano - Your Choice!.srt5.4 KB
[Tutorialsplanet.NET].url128 bytes
1. How to Code by Yourself (part 1).mp424.5 MB
1. How to Code by Yourself (part 1).srt22.8 KB
2. How to Code by Yourself (part 2).mp414.8 MB
2. How to Code by Yourself (part 2).srt13.3 KB
3. Proof that using Jupyter Notebook is the same as not using it.mp478.3 MB
3. Proof that using Jupyter Notebook is the same as not using it.srt14.1 KB
4. Python 2 vs Python 3.mp47.8 MB
4. Python 2 vs Python 3.srt6.1 KB
5. Is Theano Dead.mp417.8 MB
5. Is Theano Dead.srt12.9 KB
[Tutorialsplanet.NET].url128 bytes
1. How to Succeed in this Course (Long Version).mp418.3 MB
1. How to Succeed in this Course (Long Version).srt14.5 KB
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp40 bytes
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt0 bytes
3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp429.3 MB
3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt16.0 KB
4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp437.6 MB
4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt23.0 KB
1. What is the Appendix.mp45.5 MB
1. What is the Appendix.srt3.7 KB
2. BONUS.mp437.9 MB
2. BONUS.srt7.9 KB
1. Reinforcement Learning Section Introduction.mp441.0 MB
1. Reinforcement Learning Section Introduction.srt8.8 KB
10. Epsilon-Greedy.mp440.2 MB
10. Epsilon-Greedy.srt7.5 KB
11. Q-Learning.mp467.1 MB
11. Q-Learning.srt19.0 KB
12. How to Learn Reinforcement Learning.mp440.6 MB
12. How to Learn Reinforcement Learning.srt7.8 KB
13. Suggestion Box.mp416.1 MB
13. Suggestion Box.srt4.7 KB
2. Elements of a Reinforcement Learning Problem.mp4105.2 MB
2. Elements of a Reinforcement Learning Problem.srt27.1 KB
3. States, Actions, Rewards, Policies.mp444.5 MB
3. States, Actions, Rewards, Policies.srt11.7 KB
4. Markov Decision Processes (MDPs).mp450.9 MB
4. Markov Decision Processes (MDPs).srt13.3 KB
5. The Return.mp423.8 MB
5. The Return.srt6.7 KB
6. Value Functions and the Bellman Equation.mp448.1 MB
6. Value Functions and the Bellman Equation.srt12.8 KB
7. What does it mean to “learn”.mp431.8 MB
7. What does it mean to “learn”.srt8.9 KB
8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp442.9 MB
8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt12.4 KB
9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp457.3 MB
9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt15.5 KB
1. OpenAI Gym Tutorial.mp48.7 MB
1. OpenAI Gym Tutorial.srt7.7 KB
10. Theano Warmup.mp45.8 MB
10. Theano Warmup.srt3.5 KB
11. Tensorflow Warmup.mp45.1 MB
11. Tensorflow Warmup.srt2.5 KB
12. Plugging in a Neural Network.mp45.9 MB
12. Plugging in a Neural Network.srt4.8 KB
13. OpenAI Gym Section Summary.mp45.3 MB
13. OpenAI Gym Section Summary.srt4.2 KB
2. Random Search.mp410.3 MB
2. Random Search.srt6.9 KB
3. Saving a Video.mp44.5 MB
3. Saving a Video.srt2.4 KB
4. CartPole with Bins (Theory).mp46.0 MB
4. CartPole with Bins (Theory).srt5.2 KB
5. CartPole with Bins (Code).mp414.7 MB
5. CartPole with Bins (Code).srt8.0 KB
6. RBF Neural Networks.mp416.5 MB
6. RBF Neural Networks.srt14.6 KB
7. RBF Networks with Mountain Car (Code).mp413.8 MB
7. RBF Networks with Mountain Car (Code).srt6.4 KB
8. RBF Networks with CartPole (Theory).mp43.1 MB
8. RBF Networks with CartPole (Theory).srt2.4 KB
9. RBF Networks with CartPole (Code).mp48.9 MB
9. RBF Networks with CartPole (Code).srt3.6 KB
1. N-Step Methods.mp415.6 MB
1. N-Step Methods.srt3.8 KB
2. N-Step in Code.mp49.5 MB
2. N-Step in Code.srt4.2 KB
3. TD Lambda.mp411.8 MB
3. TD Lambda.srt9.3 KB
4. TD Lambda in Code.mp47.6 MB
4. TD Lambda in Code.srt3.3 KB
5. TD Lambda Summary.mp43.6 MB
5. TD Lambda Summary.srt3.0 KB
1. Policy Gradient Methods.mp417.9 MB
1. Policy Gradient Methods.srt14.8 KB
10. Policy Gradient Section Summary.mp43.3 MB
10. Policy Gradient Section Summary.srt1.9 KB
2. Policy Gradient in TensorFlow for CartPole.mp418.0 MB
2. Policy Gradient in TensorFlow for CartPole.srt8.7 KB
3. Policy Gradient in Theano for CartPole.mp413.4 MB
3. Policy Gradient in Theano for CartPole.srt4.5 KB
4. Continuous Action Spaces.mp46.6 MB
4. Continuous Action Spaces.srt5.3 KB
5. Mountain Car Continuous Specifics.mp46.5 MB
5. Mountain Car Continuous Specifics.srt5.0 KB
6. Mountain Car Continuous Theano.mp419.1 MB
6. Mountain Car Continuous Theano.srt9.9 KB
7. Mountain Car Continuous Tensorflow.mp420.1 MB
7. Mountain Car Continuous Tensorflow.srt10.3 KB
8. Mountain Car Continuous Tensorflow (v2).mp418.8 MB
8. Mountain Car Continuous Tensorflow (v2).srt7.1 KB
9. Mountain Car Continuous Theano (v2).mp422.2 MB
9. Mountain Car Continuous Theano (v2).srt8.3 KB
1. Deep Q-Learning Intro.mp45.9 MB
1. Deep Q-Learning Intro.srt4.8 KB
10. Deep Q-Learning Section Summary.mp410.4 MB
10. Deep Q-Learning Section Summary.srt6.0 KB
2. Deep Q-Learning Techniques.mp414.4 MB
2. Deep Q-Learning Techniques.srt12.3 KB
3. Deep Q-Learning in Tensorflow for CartPole.mp415.0 MB
3. Deep Q-Learning in Tensorflow for CartPole.srt5.8 KB
4. Deep Q-Learning in Theano for CartPole.mp413.8 MB
4. Deep Q-Learning in Theano for CartPole.srt5.4 KB
5. Additional Implementation Details for Atari.mp48.5 MB
5. Additional Implementation Details for Atari.srt7.0 KB
6. Pseudocode and Replay Memory.mp427.8 MB
6. Pseudocode and Replay Memory.srt7.8 KB
7. Deep Q-Learning in Tensorflow for Breakout.mp4234.6 MB
7. Deep Q-Learning in Tensorflow for Breakout.srt28.2 KB
8. Deep Q-Learning in Theano for Breakout.mp4233.7 MB
8. Deep Q-Learning in Theano for Breakout.srt28.1 KB
9. Partially Observable MDPs.mp47.6 MB
9. Partially Observable MDPs.srt5.8 KB
1. A3C - Theory and Outline.mp471.8 MB
1. A3C - Theory and Outline.srt20.3 KB
2. A3C - Code pt 1 (Warmup).mp450.1 MB
2. A3C - Code pt 1 (Warmup).srt7.8 KB
3. A3C - Code pt 2.mp457.6 MB
3. A3C - Code pt 2.srt8.3 KB
4. A3C - Code pt 3.mp484.5 MB
4. A3C - Code pt 3.srt9.0 KB
5. A3C - Code pt 4.mp4184.3 MB
5. A3C - Code pt 4.srt21.2 KB
6. A3C - Section Summary.mp48.9 MB
6. A3C - Section Summary.srt2.6 KB
7. Course Summary.mp49.4 MB
7. Course Summary.srt6.0 KB
1. (Review) Theano Basics.mp478.1 MB
1. (Review) Theano Basics.srt7.3 KB
2. (Review) Theano Neural Network in Code.mp467.7 MB
2. (Review) Theano Neural Network in Code.srt3.9 KB
3. (Review) Tensorflow Basics.mp463.4 MB
3. (Review) Tensorflow Basics.srt6.0 KB
4. (Review) Tensorflow Neural Network in Code.mp478.4 MB
4. (Review) Tensorflow Neural Network in Code.srt6.0 KB
1. Anaconda Environment Setup.mp4186.2 MB
1. Anaconda Environment Setup.srt20.1 KB
2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.9 MB
2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt14.5 KB
[Tutorialsplanet.NET].url128 bytes
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