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

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

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文件大小： 2.28G
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收录时间：2021-03-24
最近下载：2025-08-29

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文件列表

  • 6. Deep Q-Learning/7. Deep Q-Learning in Tensorflow for Breakout.mp4 246.0 MB
  • 6. Deep Q-Learning/8. Deep Q-Learning in Theano for Breakout.mp4 245.0 MB
  • 9. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4 195.2 MB
  • 7. A3C/5. A3C - Code pt 4.mp4 193.3 MB
  • 8. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.mp4 102.0 MB
  • 8. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.mp4 97.9 MB
  • 8. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.mp4 91.2 MB
  • 7. A3C/4. A3C - Code pt 3.mp4 88.6 MB
  • 8. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.mp4 85.4 MB
  • 9. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.mp4 82.0 MB
  • 7. A3C/1. A3C - Theory and Outline.mp4 75.2 MB
  • 7. A3C/3. A3C - Code pt 2.mp4 60.4 MB
  • 7. A3C/2. A3C - Code pt 1 (Warmup).mp4 52.5 MB
  • 9. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.0 MB
  • 9. Appendix FAQ/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.8 MB
  • 9. Appendix FAQ/13. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
  • 9. Appendix FAQ/12. What order should I take your courses in (part 2).mp4 39.4 MB
  • 9. Appendix FAQ/11. What order should I take your courses in (part 1).mp4 30.7 MB
  • 6. Deep Q-Learning/6. Pseudocode and Replay Memory.mp4 29.2 MB
  • 9. Appendix FAQ/4. How to Code by Yourself (part 1).mp4 25.7 MB
  • 5. Policy Gradients/9. Mountain Car Continuous Theano (v2).mp4 23.3 MB
  • 5. Policy Gradients/7. Mountain Car Continuous Tensorflow.mp4 21.1 MB
  • 5. Policy Gradients/6. Mountain Car Continuous Theano.mp4 20.0 MB
  • 1. Introduction and Logistics/4. Tensorflow or Theano - Your Choice!.mp4 19.8 MB
  • 5. Policy Gradients/8. Mountain Car Continuous Tensorflow (v2).mp4 19.7 MB
  • 2. Background Review/5. Review of Temporal Difference Learning.mp4 19.4 MB
  • 9. Appendix FAQ/6. How to Succeed in this Course (Long Version).mp4 19.2 MB
  • 5. Policy Gradients/2. Policy Gradient in TensorFlow for CartPole.mp4 18.8 MB
  • 5. Policy Gradients/1. Policy Gradient Methods.mp4 18.8 MB
  • 9. Appendix FAQ/10. Is Theano Dead.mp4 18.7 MB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/6. RBF Neural Networks.mp4 17.3 MB
  • 1. Introduction and Logistics/1. Introduction and Outline.mp4 16.6 MB
  • 6. Deep Q-Learning/3. Deep Q-Learning in Tensorflow for CartPole.mp4 15.7 MB
  • 9. Appendix FAQ/5. How to Code by Yourself (part 2).mp4 15.5 MB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/5. CartPole with Bins (Code).mp4 15.4 MB
  • 6. Deep Q-Learning/2. Deep Q-Learning Techniques.mp4 15.1 MB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/7. RBF Networks with Mountain Car (Code).mp4 14.4 MB
  • 6. Deep Q-Learning/4. Deep Q-Learning in Theano for CartPole.mp4 14.4 MB
  • 5. Policy Gradients/3. Policy Gradient in Theano for CartPole.mp4 14.1 MB
  • 2. Background Review/2. Review of Markov Decision Processes.mp4 12.9 MB
  • 4. TD Lambda/3. TD Lambda.mp4 12.3 MB
  • 2. Background Review/7. Review of Deep Learning.mp4 11.6 MB
  • 6. Deep Q-Learning/10. Deep Q-Learning Section Summary.mp4 10.9 MB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/2. Random Search.mp4 10.8 MB
  • 4. TD Lambda/2. N-Step in Code.mp4 9.9 MB
  • 7. A3C/7. Course Summary.mp4 9.9 MB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/9. RBF Networks with CartPole (Code).mp4 9.4 MB
  • 7. A3C/6. A3C - Section Summary.mp4 9.3 MB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/1. OpenAI Gym Tutorial.mp4 9.1 MB
  • 6. Deep Q-Learning/5. Additional Implementation Details for Atari.mp4 8.9 MB
  • 9. Appendix FAQ/9. Python 2 vs Python 3.mp4 8.2 MB
  • 4. TD Lambda/4. TD Lambda in Code.mp4 8.0 MB
  • 6. Deep Q-Learning/9. Partially Observable MDPs.mp4 8.0 MB
  • 5. Policy Gradients/4. Continuous Action Spaces.mp4 6.9 MB
  • 2. Background Review/3. Review of Dynamic Programming.mp4 6.8 MB
  • 5. Policy Gradients/5. Mountain Car Continuous Specifics.mp4 6.8 MB
  • 2. Background Review/4. Review of Monte Carlo Methods.mp4 6.5 MB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/4. CartPole with Bins (Theory).mp4 6.3 MB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/12. Plugging in a Neural Network.mp4 6.2 MB
  • 6. Deep Q-Learning/1. Deep Q-Learning Intro.mp4 6.2 MB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/10. Theano Warmup.mp4 6.1 MB
  • 9. Appendix FAQ/1. What is the Appendix.mp4 5.7 MB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/13. OpenAI Gym Section Summary.mp4 5.6 MB
  • 1. Introduction and Logistics/2. Where to get the Code.mp4 5.4 MB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/11. Tensorflow Warmup.mp4 5.3 MB
  • 4. TD Lambda/1. N-Step Methods.mp4 5.3 MB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/3. Saving a Video.mp4 4.8 MB
  • 2. Background Review/1. Review Intro.mp4 4.4 MB
  • 2. Background Review/6. Review of Approximation Methods for Reinforcement Learning.mp4 3.8 MB
  • 4. TD Lambda/5. TD Lambda Summary.mp4 3.8 MB
  • 5. Policy Gradients/10. Policy Gradient Section Summary.mp4 3.5 MB
  • 1. Introduction and Logistics/3. How to Succeed in this Course.mp4 3.5 MB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/8. RBF Networks with CartPole (Theory).mp4 3.2 MB
  • 9. Appendix FAQ/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 32.5 kB
  • 6. Deep Q-Learning/7. Deep Q-Learning in Tensorflow for Breakout.srt 28.8 kB
  • 6. Deep Q-Learning/8. Deep Q-Learning in Theano for Breakout.srt 28.8 kB
  • 9. Appendix FAQ/12. What order should I take your courses in (part 2).srt 23.6 kB
  • 9. Appendix FAQ/4. How to Code by Yourself (part 1).srt 23.3 kB
  • 7. A3C/5. A3C - Code pt 4.srt 21.7 kB
  • 7. A3C/1. A3C - Theory and Outline.srt 20.7 kB
  • 9. Appendix FAQ/2. Windows-Focused Environment Setup 2018.srt 20.6 kB
  • 9. Appendix FAQ/11. What order should I take your courses in (part 1).srt 16.4 kB
  • 5. Policy Gradients/1. Policy Gradient Methods.srt 15.2 kB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/6. RBF Neural Networks.srt 15.0 kB
  • 9. Appendix FAQ/6. How to Succeed in this Course (Long Version).srt 14.9 kB
  • 1. Introduction and Logistics/1. Introduction and Outline.srt 14.8 kB
  • 9. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14.8 kB
  • 9. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.srt 14.5 kB
  • 9. Appendix FAQ/5. How to Code by Yourself (part 2).srt 13.6 kB
  • 9. Appendix FAQ/10. Is Theano Dead.srt 13.2 kB
  • 6. Deep Q-Learning/2. Deep Q-Learning Techniques.srt 12.6 kB
  • 5. Policy Gradients/7. Mountain Car Continuous Tensorflow.srt 10.5 kB
  • 2. Background Review/2. Review of Markov Decision Processes.srt 10.3 kB
  • 5. Policy Gradients/6. Mountain Car Continuous Theano.srt 10.1 kB
  • 4. TD Lambda/3. TD Lambda.srt 9.6 kB
  • 2. Background Review/7. Review of Deep Learning.srt 9.4 kB
  • 7. A3C/4. A3C - Code pt 3.srt 9.2 kB
  • 5. Policy Gradients/2. Policy Gradient in TensorFlow for CartPole.srt 8.9 kB
  • 5. Policy Gradients/9. Mountain Car Continuous Theano (v2).srt 8.5 kB
  • 7. A3C/3. A3C - Code pt 2.srt 8.5 kB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/5. CartPole with Bins (Code).srt 8.2 kB
  • 9. Appendix FAQ/13. BONUS Where to get Udemy coupons and FREE deep learning material.srt 8.1 kB
  • 6. Deep Q-Learning/6. Pseudocode and Replay Memory.srt 8.0 kB
  • 7. A3C/2. A3C - Code pt 1 (Warmup).srt 8.0 kB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/1. OpenAI Gym Tutorial.srt 7.9 kB
  • 8. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.srt 7.4 kB
  • 5. Policy Gradients/8. Mountain Car Continuous Tensorflow (v2).srt 7.2 kB
  • 6. Deep Q-Learning/5. Additional Implementation Details for Atari.srt 7.1 kB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/2. Random Search.srt 7.0 kB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/7. RBF Networks with Mountain Car (Code).srt 6.6 kB
  • 9. Appendix FAQ/9. Python 2 vs Python 3.srt 6.2 kB
  • 6. Deep Q-Learning/10. Deep Q-Learning Section Summary.srt 6.2 kB
  • 7. A3C/7. Course Summary.srt 6.2 kB
  • 8. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.srt 6.0 kB
  • 2. Background Review/5. Review of Temporal Difference Learning.srt 6.0 kB
  • 6. Deep Q-Learning/3. Deep Q-Learning in Tensorflow for CartPole.srt 6.0 kB
  • 6. Deep Q-Learning/9. Partially Observable MDPs.srt 5.9 kB
  • 8. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.srt 5.6 kB
  • 6. Deep Q-Learning/4. Deep Q-Learning in Theano for CartPole.srt 5.5 kB
  • 1. Introduction and Logistics/4. Tensorflow or Theano - Your Choice!.srt 5.5 kB
  • 2. Background Review/3. Review of Dynamic Programming.srt 5.5 kB
  • 5. Policy Gradients/4. Continuous Action Spaces.srt 5.4 kB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/4. CartPole with Bins (Theory).srt 5.3 kB
  • 5. Policy Gradients/5. Mountain Car Continuous Specifics.srt 5.1 kB
  • 2. Background Review/4. Review of Monte Carlo Methods.srt 5.1 kB
  • 6. Deep Q-Learning/1. Deep Q-Learning Intro.srt 5.0 kB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/12. Plugging in a Neural Network.srt 4.9 kB
  • 1. Introduction and Logistics/2. Where to get the Code.srt 4.7 kB
  • 5. Policy Gradients/3. Policy Gradient in Theano for CartPole.srt 4.6 kB
  • 4. TD Lambda/2. N-Step in Code.srt 4.3 kB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/13. OpenAI Gym Section Summary.srt 4.3 kB
  • 1. Introduction and Logistics/3. How to Succeed in this Course.srt 4.1 kB
  • 4. TD Lambda/1. N-Step Methods.srt 3.9 kB
  • 8. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.srt 3.9 kB
  • 9. Appendix FAQ/1. What is the Appendix.srt 3.8 kB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/9. RBF Networks with CartPole (Code).srt 3.7 kB
  • 2. Background Review/1. Review Intro.srt 3.6 kB
  • 3. OpenAI Gym and Basic Reinforcement Learning Techniques/10. Theano Warmup.srt 3.6 kB
  • 4. TD Lambda/4. TD Lambda in Code.srt 3.4 kB
  • 4. TD Lambda/5. TD Lambda Summary.srt 3.0 kB
  • 2. Background Review/6. Review of Approximation Methods for Reinforcement Learning.srt 2.9 kB
  • 7. A3C/6. A3C - Section Summary.srt 2.7 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 2.5 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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