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| [Tutorialsplanet.NET].url |
128б |
| [Tutorialsplanet.NET].url |
128б |
| [Tutorialsplanet.NET].url |
128б |
| 1. (Review) Theano Basics.mp4 |
78.08Мб |
| 1. (Review) Theano Basics.srt |
7.28Кб |
| 1. A3C - Theory and Outline.mp4 |
71.76Мб |
| 1. A3C - Theory and Outline.srt |
20.25Кб |
| 1. Anaconda Environment Setup.mp4 |
186.17Мб |
| 1. Anaconda Environment Setup.srt |
20.10Кб |
| 1. Deep Q-Learning Intro.mp4 |
5.90Мб |
| 1. Deep Q-Learning Intro.srt |
4.84Кб |
| 1. How to Code by Yourself (part 1).mp4 |
24.53Мб |
| 1. How to Code by Yourself (part 1).srt |
22.75Кб |
| 1. How to Succeed in this Course (Long Version).mp4 |
18.32Мб |
| 1. How to Succeed in this Course (Long Version).srt |
14.55Кб |
| 1. Introduction and Outline.mp4 |
50.49Мб |
| 1. Introduction and Outline.srt |
11.29Кб |
| 1. N-Step Methods.mp4 |
15.56Мб |
| 1. N-Step Methods.srt |
3.80Кб |
| 1. OpenAI Gym Tutorial.mp4 |
8.67Мб |
| 1. OpenAI Gym Tutorial.srt |
7.68Кб |
| 1. Policy Gradient Methods.mp4 |
17.94Мб |
| 1. Policy Gradient Methods.srt |
14.85Кб |
| 1. Reinforcement Learning Section Introduction.mp4 |
40.97Мб |
| 1. Reinforcement Learning Section Introduction.srt |
8.82Кб |
| 1. What is the Appendix.mp4 |
5.45Мб |
| 1. What is the Appendix.srt |
3.72Кб |
| 10. Deep Q-Learning Section Summary.mp4 |
10.40Мб |
| 10. Deep Q-Learning Section Summary.srt |
6.02Кб |
| 10. Epsilon-Greedy.mp4 |
40.16Мб |
| 10. Epsilon-Greedy.srt |
7.49Кб |
| 10. Policy Gradient Section Summary.mp4 |
3.33Мб |
| 10. Policy Gradient Section Summary.srt |
1.86Кб |
| 10. Theano Warmup.mp4 |
5.83Мб |
| 10. Theano Warmup.srt |
3.49Кб |
| 11. Q-Learning.mp4 |
67.09Мб |
| 11. Q-Learning.srt |
18.96Кб |
| 11. Tensorflow Warmup.mp4 |
5.06Мб |
| 11. Tensorflow Warmup.srt |
2.47Кб |
| 12. How to Learn Reinforcement Learning.mp4 |
40.56Мб |
| 12. How to Learn Reinforcement Learning.srt |
7.85Кб |
| 12. Plugging in a Neural Network.mp4 |
5.91Мб |
| 12. Plugging in a Neural Network.srt |
4.83Кб |
| 13. OpenAI Gym Section Summary.mp4 |
5.31Мб |
| 13. OpenAI Gym Section Summary.srt |
4.16Кб |
| 13. Suggestion Box.mp4 |
16.14Мб |
| 13. Suggestion Box.srt |
4.70Кб |
| 2. (Review) Theano Neural Network in Code.mp4 |
67.75Мб |
| 2. (Review) Theano Neural Network in Code.srt |
3.87Кб |
| 2.1 Github Link.html |
120б |
| 2. A3C - Code pt 1 (Warmup).mp4 |
50.09Мб |
| 2. A3C - Code pt 1 (Warmup).srt |
7.79Кб |
| 2. BONUS.mp4 |
37.85Мб |
| 2. BONUS.srt |
7.87Кб |
| 2. Deep Q-Learning Techniques.mp4 |
14.44Мб |
| 2. Deep Q-Learning Techniques.srt |
12.27Кб |
| 2. Elements of a Reinforcement Learning Problem.mp4 |
105.24Мб |
| 2. Elements of a Reinforcement Learning Problem.srt |
27.14Кб |
| 2. How to Code by Yourself (part 2).mp4 |
14.81Мб |
| 2. How to Code by Yourself (part 2).srt |
13.26Кб |
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 |
43.92Мб |
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt |
14.48Кб |
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 |
0б |
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt |
0б |
| 2. N-Step in Code.mp4 |
9.47Мб |
| 2. N-Step in Code.srt |
4.23Кб |
| 2. Policy Gradient in TensorFlow for CartPole.mp4 |
17.97Мб |
| 2. Policy Gradient in TensorFlow for CartPole.srt |
8.67Кб |
| 2. Random Search.mp4 |
10.29Мб |
| 2. Random Search.srt |
6.86Кб |
| 2. Where to get the Code.mp4 |
51.63Мб |
| 2. Where to get the Code.srt |
13.17Кб |
| 3. (Review) Tensorflow Basics.mp4 |
63.38Мб |
| 3. (Review) Tensorflow Basics.srt |
5.96Кб |
| 3. A3C - Code pt 2.mp4 |
57.61Мб |
| 3. A3C - Code pt 2.srt |
8.31Кб |
| 3. Deep Q-Learning in Tensorflow for CartPole.mp4 |
14.98Мб |
| 3. Deep Q-Learning in Tensorflow for CartPole.srt |
5.83Кб |
| 3. How to Succeed in this Course.mp4 |
43.81Мб |
| 3. How to Succeed in this Course.srt |
8.28Кб |
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 |
29.33Мб |
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt |
16.03Кб |
| 3. Policy Gradient in Theano for CartPole.mp4 |
13.44Мб |
| 3. Policy Gradient in Theano for CartPole.srt |
4.51Кб |
| 3. Proof that using Jupyter Notebook is the same as not using it.mp4 |
78.25Мб |
| 3. Proof that using Jupyter Notebook is the same as not using it.srt |
14.12Кб |
| 3. Saving a Video.mp4 |
4.54Мб |
| 3. Saving a Video.srt |
2.37Кб |
| 3. States, Actions, Rewards, Policies.mp4 |
44.52Мб |
| 3. States, Actions, Rewards, Policies.srt |
11.72Кб |
| 3. TD Lambda.mp4 |
11.78Мб |
| 3. TD Lambda.srt |
9.35Кб |
| 4. (Review) Tensorflow Neural Network in Code.mp4 |
78.44Мб |
| 4. (Review) Tensorflow Neural Network in Code.srt |
5.98Кб |
| 4. A3C - Code pt 3.mp4 |
84.52Мб |
| 4. A3C - Code pt 3.srt |
8.96Кб |
| 4. CartPole with Bins (Theory).mp4 |
6.03Мб |
| 4. CartPole with Bins (Theory).srt |
5.22Кб |
| 4. Continuous Action Spaces.mp4 |
6.58Мб |
| 4. Continuous Action Spaces.srt |
5.30Кб |
| 4. Deep Q-Learning in Theano for CartPole.mp4 |
13.77Мб |
| 4. Deep Q-Learning in Theano for CartPole.srt |
5.42Кб |
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 |
37.63Мб |
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt |
23.04Кб |
| 4. Markov Decision Processes (MDPs).mp4 |
50.87Мб |
| 4. Markov Decision Processes (MDPs).srt |
13.32Кб |
| 4. Python 2 vs Python 3.mp4 |
7.84Мб |
| 4. Python 2 vs Python 3.srt |
6.10Кб |
| 4. TD Lambda in Code.mp4 |
7.62Мб |
| 4. TD Lambda in Code.srt |
3.35Кб |
| 4. Tensorflow or Theano - Your Choice!.mp4 |
18.93Мб |
| 4. Tensorflow or Theano - Your Choice!.srt |
5.40Кб |
| 5. A3C - Code pt 4.mp4 |
184.34Мб |
| 5. A3C - Code pt 4.srt |
21.21Кб |
| 5. Additional Implementation Details for Atari.mp4 |
8.51Мб |
| 5. Additional Implementation Details for Atari.srt |
6.97Кб |
| 5. CartPole with Bins (Code).mp4 |
14.70Мб |
| 5. CartPole with Bins (Code).srt |
7.99Кб |
| 5. Is Theano Dead.mp4 |
17.81Мб |
| 5. Is Theano Dead.srt |
12.92Кб |
| 5. Mountain Car Continuous Specifics.mp4 |
6.50Мб |
| 5. Mountain Car Continuous Specifics.srt |
5.01Кб |
| 5. TD Lambda Summary.mp4 |
3.65Мб |
| 5. TD Lambda Summary.srt |
2.97Кб |
| 5. The Return.mp4 |
23.77Мб |
| 5. The Return.srt |
6.66Кб |
| 6. A3C - Section Summary.mp4 |
8.86Мб |
| 6. A3C - Section Summary.srt |
2.65Кб |
| 6. Mountain Car Continuous Theano.mp4 |
19.06Мб |
| 6. Mountain Car Continuous Theano.srt |
9.87Кб |
| 6. Pseudocode and Replay Memory.mp4 |
27.81Мб |
| 6. Pseudocode and Replay Memory.srt |
7.81Кб |
| 6. RBF Neural Networks.mp4 |
16.51Мб |
| 6. RBF Neural Networks.srt |
14.64Кб |
| 6. Value Functions and the Bellman Equation.mp4 |
48.15Мб |
| 6. Value Functions and the Bellman Equation.srt |
12.85Кб |
| 7. Course Summary.mp4 |
9.45Мб |
| 7. Course Summary.srt |
6.02Кб |
| 7. Deep Q-Learning in Tensorflow for Breakout.mp4 |
234.61Мб |
| 7. Deep Q-Learning in Tensorflow for Breakout.srt |
28.16Кб |
| 7. Mountain Car Continuous Tensorflow.mp4 |
20.09Мб |
| 7. Mountain Car Continuous Tensorflow.srt |
10.30Кб |
| 7. RBF Networks with Mountain Car (Code).mp4 |
13.76Мб |
| 7. RBF Networks with Mountain Car (Code).srt |
6.41Кб |
| 7. What does it mean to “learn”.mp4 |
31.83Мб |
| 7. What does it mean to “learn”.srt |
8.91Кб |
| 8. Deep Q-Learning in Theano for Breakout.mp4 |
233.70Мб |
| 8. Deep Q-Learning in Theano for Breakout.srt |
28.10Кб |
| 8. Mountain Car Continuous Tensorflow (v2).mp4 |
18.78Мб |
| 8. Mountain Car Continuous Tensorflow (v2).srt |
7.06Кб |
| 8. RBF Networks with CartPole (Theory).mp4 |
3.05Мб |
| 8. RBF Networks with CartPole (Theory).srt |
2.45Кб |
| 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 |
42.89Мб |
| 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt |
12.41Кб |
| 9. Mountain Car Continuous Theano (v2).mp4 |
22.20Мб |
| 9. Mountain Car Continuous Theano (v2).srt |
8.34Кб |
| 9. Partially Observable MDPs.mp4 |
7.60Мб |
| 9. Partially Observable MDPs.srt |
5.76Кб |
| 9. RBF Networks with CartPole (Code).mp4 |
8.91Мб |
| 9. RBF Networks with CartPole (Code).srt |
3.62Кб |
| 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 |
57.31Мб |
| 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt |
15.55Кб |