[DesireCourse.Net] Udemy - Advanced AI Deep Reinforcement Learning in Python

Creation Time	Aug. 26, 2023, 7:16 p.m.
Last Access Time	Nov. 10, 2024, 5:03 p.m.
File Size	2.3 GB
Keywords	AI DesireCourse - Deep Udemy Python Reinforcement Learning in Net Advanced
Total Requests	67
Total Files	149
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6. Deep Q-Learning/7. Deep Q-Learning in Tensorflow for Breakout.mp4 234.6 MB
6. Deep Q-Learning/8. Deep Q-Learning in Theano for Breakout.mp4 233.7 MB
9. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4 186.2 MB
7. A3C/5. A3C - Code pt 4.mp4 184.3 MB
8. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.mp4 97.3 MB
8. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.mp4 93.4 MB
8. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.mp4 87.0 MB
7. A3C/4. A3C - Code pt 3.mp4 84.5 MB
8. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.mp4 81.4 MB
9. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.mp4 78.2 MB
7. A3C/1. A3C - Theory and Outline.mp4 71.8 MB
7. A3C/3. A3C - Code pt 2.mp4 57.6 MB
7. A3C/2. A3C - Code pt 1 (Warmup).mp4 50.1 MB
9. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.9 MB
9. Appendix FAQ/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39.0 MB
9. Appendix FAQ/13. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 37.8 MB
9. Appendix FAQ/12. What order should I take your courses in (part 2).mp4 37.6 MB
9. Appendix FAQ/11. What order should I take your courses in (part 1).mp4 29.3 MB
6. Deep Q-Learning/6. Pseudocode and Replay Memory.mp4 27.8 MB
9. Appendix FAQ/4. How to Code by Yourself (part 1).mp4 24.5 MB
5. Policy Gradients/9. Mountain Car Continuous Theano (v2).mp4 22.2 MB
5. Policy Gradients/7. Mountain Car Continuous Tensorflow.mp4 20.1 MB
5. Policy Gradients/6. Mountain Car Continuous Theano.mp4 19.1 MB
1. Introduction and Logistics/4. Tensorflow or Theano - Your Choice!.mp4 18.9 MB
5. Policy Gradients/8. Mountain Car Continuous Tensorflow (v2).mp4 18.8 MB
2. Background Review/5. Review of Temporal Difference Learning.mp4 18.5 MB
9. Appendix FAQ/6. How to Succeed in this Course (Long Version).mp4 18.3 MB
5. Policy Gradients/2. Policy Gradient in TensorFlow for CartPole.mp4 18.0 MB
5. Policy Gradients/1. Policy Gradient Methods.mp4 17.9 MB
9. Appendix FAQ/10. Is Theano Dead.mp4 17.8 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/6. RBF Neural Networks.mp4 16.5 MB
1. Introduction and Logistics/1. Introduction and Outline.mp4 15.8 MB
6. Deep Q-Learning/3. Deep Q-Learning in Tensorflow for CartPole.mp4 15.0 MB
9. Appendix FAQ/5. How to Code by Yourself (part 2).mp4 14.8 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/5. CartPole with Bins (Code).mp4 14.7 MB
6. Deep Q-Learning/2. Deep Q-Learning Techniques.mp4 14.4 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/7. RBF Networks with Mountain Car (Code).mp4 13.8 MB
6. Deep Q-Learning/4. Deep Q-Learning in Theano for CartPole.mp4 13.8 MB
5. Policy Gradients/3. Policy Gradient in Theano for CartPole.mp4 13.4 MB
2. Background Review/2. Review of Markov Decision Processes.mp4 12.3 MB
4. TD Lambda/3. TD Lambda.mp4 11.8 MB
2. Background Review/7. Review of Deep Learning.mp4 11.0 MB
6. Deep Q-Learning/10. Deep Q-Learning Section Summary.mp4 10.4 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/2. Random Search.mp4 10.3 MB
4. TD Lambda/2. N-Step in Code.mp4 9.5 MB
7. A3C/7. Course Summary.mp4 9.4 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/9. RBF Networks with CartPole (Code).mp4 8.9 MB
7. A3C/6. A3C - Section Summary.mp4 8.9 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/1. OpenAI Gym Tutorial.mp4 8.7 MB
6. Deep Q-Learning/5. Additional Implementation Details for Atari.mp4 8.5 MB
9. Appendix FAQ/9. Python 2 vs Python 3.mp4 7.8 MB
4. TD Lambda/4. TD Lambda in Code.mp4 7.6 MB
6. Deep Q-Learning/9. Partially Observable MDPs.mp4 7.6 MB
5. Policy Gradients/4. Continuous Action Spaces.mp4 6.6 MB
2. Background Review/3. Review of Dynamic Programming.mp4 6.5 MB
5. Policy Gradients/5. Mountain Car Continuous Specifics.mp4 6.5 MB
2. Background Review/4. Review of Monte Carlo Methods.mp4 6.2 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/4. CartPole with Bins (Theory).mp4 6.0 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/12. Plugging in a Neural Network.mp4 5.9 MB
6. Deep Q-Learning/1. Deep Q-Learning Intro.mp4 5.9 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/10. Theano Warmup.mp4 5.8 MB
9. Appendix FAQ/1. What is the Appendix.mp4 5.5 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/13. OpenAI Gym Section Summary.mp4 5.3 MB
1. Introduction and Logistics/2. Where to get the Code.mp4 5.2 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/11. Tensorflow Warmup.mp4 5.1 MB
4. TD Lambda/1. N-Step Methods.mp4 5.0 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/3. Saving a Video.mp4 4.5 MB
2. Background Review/1. Review Intro.mp4 4.2 MB
2. Background Review/6. Review of Approximation Methods for Reinforcement Learning.mp4 3.7 MB
4. TD Lambda/5. TD Lambda Summary.mp4 3.6 MB
5. Policy Gradients/10. Policy Gradient Section Summary.mp4 3.3 MB
1. Introduction and Logistics/3. How to Succeed in this Course.mp4 3.3 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/8. RBF Networks with CartPole (Theory).mp4 3.1 MB
9. Appendix FAQ/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 31.8 KB
6. Deep Q-Learning/7. Deep Q-Learning in Tensorflow for Breakout.srt 28.2 KB
6. Deep Q-Learning/8. Deep Q-Learning in Theano for Breakout.srt 28.1 KB
9. Appendix FAQ/12. What order should I take your courses in (part 2).srt 23.0 KB
9. Appendix FAQ/4. How to Code by Yourself (part 1).srt 22.8 KB
7. A3C/5. A3C - Code pt 4.srt 21.2 KB
7. A3C/1. A3C - Theory and Outline.srt 20.3 KB
9. Appendix FAQ/2. Windows-Focused Environment Setup 2018.srt 20.1 KB
9. Appendix FAQ/11. What order should I take your courses in (part 1).srt 16.0 KB
5. Policy Gradients/1. Policy Gradient Methods.srt 14.8 KB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/6. RBF Neural Networks.srt 14.6 KB
9. Appendix FAQ/6. How to Succeed in this Course (Long Version).srt 14.5 KB
1. Introduction and Logistics/1. Introduction and Outline.srt 14.5 KB
9. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14.5 KB
9. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.srt 14.1 KB
9. Appendix FAQ/5. How to Code by Yourself (part 2).srt 13.3 KB
9. Appendix FAQ/10. Is Theano Dead.srt 12.9 KB
6. Deep Q-Learning/2. Deep Q-Learning Techniques.srt 12.3 KB
5. Policy Gradients/7. Mountain Car Continuous Tensorflow.srt 10.3 KB
2. Background Review/2. Review of Markov Decision Processes.srt 10.1 KB
5. Policy Gradients/6. Mountain Car Continuous Theano.srt 9.9 KB
4. TD Lambda/3. TD Lambda.srt 9.3 KB
2. Background Review/7. Review of Deep Learning.srt 9.2 KB
7. A3C/4. A3C - Code pt 3.srt 9.0 KB
5. Policy Gradients/2. Policy Gradient in TensorFlow for CartPole.srt 8.7 KB
5. Policy Gradients/9. Mountain Car Continuous Theano (v2).srt 8.3 KB
7. A3C/3. A3C - Code pt 2.srt 8.3 KB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/5. CartPole with Bins (Code).srt 8.0 KB
9. Appendix FAQ/13. BONUS Where to get Udemy coupons and FREE deep learning material.srt 7.9 KB
6. Deep Q-Learning/6. Pseudocode and Replay Memory.srt 7.8 KB
7. A3C/2. A3C - Code pt 1 (Warmup).srt 7.8 KB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/1. OpenAI Gym Tutorial.srt 7.7 KB
8. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.srt 7.2 KB
5. Policy Gradients/8. Mountain Car Continuous Tensorflow (v2).srt 7.1 KB
6. Deep Q-Learning/5. Additional Implementation Details for Atari.srt 7.0 KB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/2. Random Search.srt 6.9 KB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/7. RBF Networks with Mountain Car (Code).srt 6.4 KB
9. Appendix FAQ/9. Python 2 vs Python 3.srt 6.1 KB
6. Deep Q-Learning/10. Deep Q-Learning Section Summary.srt 6.0 KB
7. A3C/7. Course Summary.srt 6.0 KB
8. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.srt 5.9 KB
2. Background Review/5. Review of Temporal Difference Learning.srt 5.9 KB
6. Deep Q-Learning/3. Deep Q-Learning in Tensorflow for CartPole.srt 5.8 KB
6. Deep Q-Learning/9. Partially Observable MDPs.srt 5.8 KB
8. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.srt 5.5 KB
6. Deep Q-Learning/4. Deep Q-Learning in Theano for CartPole.srt 5.4 KB
1. Introduction and Logistics/4. Tensorflow or Theano - Your Choice!.srt 5.4 KB
2. Background Review/3. Review of Dynamic Programming.srt 5.3 KB
5. Policy Gradients/4. Continuous Action Spaces.srt 5.3 KB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/4. CartPole with Bins (Theory).srt 5.2 KB
5. Policy Gradients/5. Mountain Car Continuous Specifics.srt 5.0 KB
2. Background Review/4. Review of Monte Carlo Methods.srt 5.0 KB
6. Deep Q-Learning/1. Deep Q-Learning Intro.srt 4.8 KB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/12. Plugging in a Neural Network.srt 4.8 KB
1. Introduction and Logistics/2. Where to get the Code.srt 4.6 KB
5. Policy Gradients/3. Policy Gradient in Theano for CartPole.srt 4.5 KB
4. TD Lambda/2. N-Step in Code.srt 4.2 KB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/13. OpenAI Gym Section Summary.srt 4.2 KB
1. Introduction and Logistics/3. How to Succeed in this Course.srt 4.0 KB
4. TD Lambda/1. N-Step Methods.srt 3.8 KB
8. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.srt 3.8 KB
9. Appendix FAQ/1. What is the Appendix.srt 3.7 KB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/9. RBF Networks with CartPole (Code).srt 3.6 KB
2. Background Review/1. Review Intro.srt 3.5 KB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/10. Theano Warmup.srt 3.5 KB
4. TD Lambda/4. TD Lambda in Code.srt 3.3 KB
4. TD Lambda/5. TD Lambda Summary.srt 3.0 KB
2. Background Review/6. Review of Approximation Methods for Reinforcement Learning.srt 2.8 KB
7. A3C/6. A3C - Section Summary.srt 2.6 KB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/11. Tensorflow Warmup.srt 2.5 KB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/8. RBF Networks with CartPole (Theory).srt 2.4 KB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/3. Saving a Video.srt 2.4 KB
5. Policy Gradients/10. Policy Gradient Section Summary.srt 1.9 KB
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[DesireCourse.Net] Udemy - Advanced AI Deep Reinforcement Learning in Python

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