| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Introduction | |||
| 1. Introduction.en_US.srt | 7.4 KB | ||
| 1. Introduction.mp4 | 22.3 MB | ||
| 2. Reinforcement Learning series.html | 6.9 KB | ||
| 3. Google Colab.en_US.srt | 1.7 KB | ||
| 3. Google Colab.mp4 | 3.6 MB | ||
| 4. Where to begin.en_US.srt | 1.2 KB | ||
| 4. Where to begin.mp4 | 2.1 MB | ||
| 5. Complete code.html | 5.4 KB | ||
| 6. Connect with me on social media.html | 5.7 KB | ||
| __MACOSX | |||
| advanced_rl_pg_methods_complete | |||
| _2_REINFORCE_continuous.ipynb | 307.2 B | ||
| _4_proximal_policy_optimization.ipynb | 307.2 B | ||
| _5_generalized_advantage_estimation.ipynb | 307.2 B | ||
| _6_TRPO.ipynb | 307.2 B | ||
| advanced_rl_pg_methods_complete | |||
| 10 - Advantage Actor Critic (A2C) | |||
| 11 - Trust region methods | |||
| 12 - Proximal Policy Optimization (PPO) | |||
| 13 - Generalized Advantage Estimation (GAE) | |||
| 14 - Trust Region Policy Optimization (TRPO) | |||
| 15 - Final steps | |||
| 2 - Refresher The Markov Decision Process (MDP) | |||
| 10. Trajectory vs episode.en_US.srt | 1.1 KB | ||
| 10. Trajectory vs episode.mp4 | 3 MB | ||
| 11. Reward vs Return.en_US.srt | 1.6 KB | ||
| 11. Reward vs Return.mp4 | 3.2 MB | ||
| 12. Discount factor.en_US.srt | 4.1 KB | ||
| 12. Discount factor.mp4 | 8.8 MB | ||
| 13. Policy.en_US.srt | 2.1 KB | ||
| 13. Policy.mp4 | 4.5 MB | ||
| 14. State values v(s) and action values q(s,a).en_US.srt | 1.2 KB | ||
| 14. State values v(s) and action values q(s,a).mp4 | 2.6 MB | ||
| 15. Bellman equations.en_US.srt | 3 KB | ||
| 15. Bellman equations.mp4 | 7.5 MB | ||
| 16. Solving a Markov decision process.en_US.srt | 3.2 KB | ||
| 16. Solving a Markov decision process.mp4 | 8.6 MB | ||
| 3 - Refresher Monte Carlo methods | |||
| 17. Monte Carlo methods.en_US.srt | 3.3 KB | ||
| 17. Monte Carlo methods.mp4 | 8.2 MB | ||
| 18. Solving control tasks with Monte Carlo methods.en_US.srt | 7 KB | ||
| 18. Solving control tasks with Monte Carlo methods.mp4 | 15 MB | ||
| 19. On-policy Monte Carlo control.en_US.srt | 4.6 KB | ||
| 19. On-policy Monte Carlo control.mp4 | 15.1 MB | ||
| 4 - Refresher Temporal difference methods | |||
| 20. Temporal difference methods.en_US.srt | 3.6 KB | ||
| 20. Temporal difference methods.mp4 | 7.7 MB | ||
| 21. Solving control tasks with temporal difference methods.en_US.srt | 3.6 KB | ||
| 21. Solving control tasks with temporal difference methods.mp4 | 8.9 MB | ||
| 22. Monte Carlo vs temporal difference methods.en_US.srt | 1.6 KB | ||
| 22. Monte Carlo vs temporal difference methods.mp4 | 5 MB | ||
| 23. SARSA.en_US.srt | 3.9 KB | ||
| 23. SARSA.mp4 | 10.8 MB | ||
| 24. Q-Learning.en_US.srt | 2.5 KB | ||
| 24. Q-Learning.mp4 | 6.5 MB | ||
| 25. Advantages of temporal difference methods.en_US.srt | 1.2 KB | ||
| 25. Advantages of temporal difference methods.mp4 | 2.2 MB | ||
| 5 - Refresher N-step bootstrapping | |||
| 26. N-step temporal difference methods.en_US.srt | 3.4 KB | ||
| 26. N-step temporal difference methods.mp4 | 7.5 MB | ||
| 27. Where do n-step methods fit.en_US.srt | 2.7 KB | ||
| 27. Where do n-step methods fit.mp4 | 6.7 MB | ||
| 28. Effect of changing n.en_US.srt | 4.6 KB | ||
| 28. Effect of changing n.mp4 | 15.5 MB | ||
| 6 - Refresher Brief introduction to Neural Networks | |||
| 29. Function approximators.en_US.srt | 8.6 KB | ||
| 29. Function approximators.mp4 | 32.5 MB | ||
| 30. Artificial Neural Networks.en_US.srt | 3.9 KB | ||
| 30. Artificial Neural Networks.mp4 | 13.3 MB | ||
| 31. Artificial Neurons.en_US.srt | 5.8 KB | ||
| 31. Artificial Neurons.mp4 | 39.1 MB | ||
| 32. How to represent a Neural Network.en_US.srt | 7.3 KB | ||
| 32. How to represent a Neural Network.mp4 | 21.8 MB | ||
| 33. Stochastic Gradient Descent.en_US.srt | 6.4 KB | ||
| 33. Stochastic Gradient Descent.mp4 | 39.5 MB | ||
| 34. Neural Network optimization.en_US.srt | 4.4 KB | ||
| 34. Neural Network optimization.mp4 | 12.5 MB | ||
| 7 - Refresher REINFORCE | |||
| 35. Policy gradient methods.en_US.srt | 4.7 KB | ||
| 35. Policy gradient methods.mp4 | 12.7 MB | ||
| 36. Representing policies using neural networks.en_US.srt | 5.2 KB | ||
| 36. Representing policies using neural networks.mp4 | 13.4 MB | ||
| 37. Policy performance.en_US.srt | 2.6 KB | ||
| 37. Policy performance.mp4 | 10.2 MB | ||
| 38. The policy gradient theorem.en_US.srt | 3.8 KB | ||
| 38. The policy gradient theorem.mp4 | 13.6 MB | ||
| 39. REINFORCE.en_US.srt | 4.1 KB | ||
| 39. REINFORCE.mp4 | 8.1 MB | ||
| 40. Parallel learning.en_US.srt | 3.6 KB | ||
| 40. Parallel learning.mp4 | 7.6 MB | ||
| 41. Entropy regularization.en_US.srt | 6.6 KB | ||
| 41. Entropy regularization.mp4 | 13.8 MB | ||
| 42. REINFORCE 2.en_US.srt | 2.4 KB | ||
| 42. REINFORCE 2.mp4 | 6.4 MB | ||
| 8 - PyTorch Lightning | |||
| 43. PyTorch Lightning.en_US.srt | 9.3 KB | ||
| 43. PyTorch Lightning.mp4 | 23.9 MB | ||
| 44. Link to the code notebook.html | 5.6 KB | ||
| 45. Create the policy.en_US.srt | 13.7 KB | ||
| 45. Create the policy.mp4 | 95.9 MB | ||
| 46. Create the environment.en_US.srt | 9.6 KB | ||
| 46. Create the environment.mp4 | 27.3 MB | ||
| 47. Create the dataset.en_US.srt | 12.2 KB | ||
| 47. Create the dataset.mp4 | 39.5 MB | ||
| 48. Create the REINFORCE algorithm - Part 1.en_US.srt | 6.2 KB | ||
| 48. Create the REINFORCE algorithm - Part 1.mp4 | 21 MB | ||
| 49. Create the REINFORCE algorithm - Part 2.en_US.srt | 10.1 KB | ||
| 49. Create the REINFORCE algorithm - Part 2.mp4 | 42 MB | ||
| 50. Check the resulting agent.en_US.srt | 6.2 KB | ||
| 50. Check the resulting agent.mp4 | 39.4 MB | ||
| 9 - REINFORCE for continuous control tasks | |||
| 51. REINFORCE for continuous action spaces.en_US.srt | 5.8 KB | ||
| 51. REINFORCE for continuous action spaces.mp4 | 9.7 MB | ||
| 52. Link to the code notebook.html | 5.6 KB | ||
| 53. Create the policy.en_US.srt | 10.3 KB | ||
| 53. Create the policy.mp4 | 64 MB | ||
| 54. Create the inverted pendulum environment.en_US.srt | 7.8 KB | ||
| 54. Create the inverted pendulum environment.mp4 | 33.8 MB | ||
| 55. Create the dataset.en_US.srt | 6.7 KB | ||
| 55. Create the dataset.mp4 | 25.6 MB | ||
| 56. Creating the algorithm - Part 1.en_US.srt | 5.5 KB | ||
| 56. Creating the algorithm - Part 1.mp4 | 20 MB | ||
| 57. Creating the algorithm - Part 2.en_US.srt | 5.6 KB | ||
| 57. Creating the algorithm - Part 2.mp4 | 27.6 MB | ||
| 58. Check the resulting agent.en_US.srt | 2.3 KB | ||
| 58. Check the resulting agent.mp4 | 7.2 MB | ||
| 7. Elements common to all control tasks.en_US.srt | 6 KB | ||
| 7. Elements common to all control tasks.mp4 | 21.5 MB | ||
| 8. The Markov decision process (MDP).en_US.srt | 5.6 KB | ||
| 8. The Markov decision process (MDP).mp4 | 15.2 MB | ||
| 9. Types of Markov decision process.en_US.srt | 2.2 KB | ||
| 9. Types of Markov decision process.mp4 | 5.2 MB | ||
| 96. Final steps.html | 6 KB | ||
| 97. Connect with me on social media.html | 5.7 KB | ||
| 87. Trust region policy optimization 1.en_US.srt | 3.9 KB | ||
| 87. Trust region policy optimization 1.mp4 | 6.5 MB | ||
| 88. Trust region policy optimization 2.en_US.srt | 6.2 KB | ||
| 88. Trust region policy optimization 2.mp4 | 11.1 MB | ||
| 89. Link to the code notebook.html | 5.6 KB | ||
| 90. TRPO in code - Part 1.en_US.srt | 3.5 KB | ||
| 90. TRPO in code - Part 1.mp4 | 19 MB | ||
| 91. TRPO in code - Part 2.en_US.srt | 2.5 KB | ||
| 91. TRPO in code - Part 2.mp4 | 11 MB | ||
| 92. TRPO in code - Part 3.en_US.srt | 2.1 KB | ||
| 92. TRPO in code - Part 3.mp4 | 6.8 MB | ||
| 93. TRPO in code - Part 4.en_US.srt | 4.7 KB | ||
| 93. TRPO in code - Part 4.mp4 | 19.7 MB | ||
| 94. TRPO in code - Part 5.en_US.srt | 8.9 KB | ||
| 94. TRPO in code - Part 5.mp4 | 43.1 MB | ||
| 95. TRPO in code - Part 6.en_US.srt | 921.6 B | ||
| 95. TRPO in code - Part 6.mp4 | 6.7 MB | ||
| 80. Generalized Advantage Estimation.en_US.srt | 12.5 KB | ||
| 80. Generalized Advantage Estimation.mp4 | 21.2 MB | ||
| 81. Link to the code notebook.html | 5.6 KB | ||
| 82. Create the Half Cheetah environment.en_US.srt | 5 KB | ||
| 82. Create the Half Cheetah environment.mp4 | 38.7 MB | ||
| 83. Create the dataset.en_US.srt | 10 KB | ||
| 83. Create the dataset.mp4 | 40.1 MB | ||
| 84. PPO with generalized advantage estimation - Part 1.en_US.srt | 3.3 KB | ||
| 84. PPO with generalized advantage estimation - Part 1.mp4 | 15.3 MB | ||
| 85. PPO with generalized advantage estimation - Part 2.en_US.srt | 5.2 KB | ||
| 85. PPO with generalized advantage estimation - Part 2.mp4 | 33.8 MB | ||
| 86. Checking the resulting agent.en_US.srt | 1 KB | ||
| 86. Checking the resulting agent.mp4 | 10.4 MB | ||
| 73. Proximal Policy Optimization.en_US.srt | 9.9 KB | ||
| 73. Proximal Policy Optimization.mp4 | 20.8 MB | ||
| 74. Link to the code notebook.html | 5.6 KB | ||
| 75. Create the environment.en_US.srt | 7.8 KB | ||
| 75. Create the environment.mp4 | 61.2 MB | ||
| 76. Create the dataset.en_US.srt | 6.7 KB | ||
| 76. Create the dataset.mp4 | 26.4 MB | ||
| 77. Create the PPO algorithm - Part 1.en_US.srt | 4.9 KB | ||
| 77. Create the PPO algorithm - Part 1.mp4 | 29.2 MB | ||
| 78. Create the PPO algorithm - Part 2.en_US.srt | 10.2 KB | ||
| 78. Create the PPO algorithm - Part 2.mp4 | 91.6 MB | ||
| 79. Check the resulting agent.en_US.srt | 1.9 KB | ||
| 79. Check the resulting agent.mp4 | 13.2 MB | ||
| 67. Line search vs trust region methods.en_US.srt | 2.6 KB | ||
| 67. Line search vs trust region methods.mp4 | 4.2 MB | ||
| 68. Line search methods.en_US.srt | 7.2 KB | ||
| 68. Line search methods.mp4 | 20.5 MB | ||
| 69. Trust region methods 1.en_US.srt | 3.4 KB | ||
| 69. Trust region methods 1.mp4 | 8.9 MB | ||
| 70. Kullback-Leibler divergence.en_US.srt | 4.7 KB | ||
| 70. Kullback-Leibler divergence.mp4 | 8.2 MB | ||
| 71. Trust region methods 2.en_US.srt | 11.4 KB | ||
| 71. Trust region methods 2.mp4 | 20.3 MB | ||
| 72. Trust region methods 3.en_US.srt | 3.1 KB | ||
| 72. Trust region methods 3.mp4 | 4.9 MB | ||
| 59. A2C.en_US.srt | 10.6 KB | ||
| 59. A2C.mp4 | 29.2 MB | ||
| 60. Link to the code notebook.html | 5.6 KB | ||
| 61. Create the policy and value network.en_US.srt | 4.5 KB | ||
| 61. Create the policy and value network.mp4 | 27 MB | ||
| 62. Create the environment.en_US.srt | 5.9 KB | ||
| 62. Create the environment.mp4 | 17.4 MB | ||
| 63. Create the dataset.en_US.srt | 2.5 KB | ||
| 63. Create the dataset.mp4 | 10 MB | ||
| 64. Implement A2C - Part 1.en_US.srt | 4.9 KB | ||
| 64. Implement A2C - Part 1.mp4 | 19.1 MB | ||
| 65. Implement A2C - Part 2.en_US.srt | 8.9 KB | ||
| 65. Implement A2C - Part 2.mp4 | 51.5 MB | ||
| 66. Check the resulting agent.en_US.srt | 2.3 KB | ||
| 66. Check the resulting agent.mp4 | 19.2 MB | ||
| 1_REINFORCE.ipynb | 15.5 KB | ||
| 2_REINFORCE_continuous.ipynb | 20.9 KB | ||
| 3_advantage_actor_critic.ipynb | 14.8 KB | ||
| 4_proximal_policy_optimization.ipynb | 20.3 KB | ||
| 5_generalized_advantage_estimation.ipynb | 21.2 KB | ||
| 6_TRPO.ipynb | 29 KB |
MQL4 Special Course - Two Pairs Arbitrage 2022
https://WebToolTip.com
Last updated 8/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 1h 10m | Size: 546.58 MB
An advanced MQL4 programming & algorithm trading & automatic trading system development course
What you'll learn
Key concepts of arbitrage.
How to implement the arbitrage strategy into an algorithm trading system.
How to set target profit of an algorithm trading system.
Basics of MQL4 grammar such as variables, functions, and statements.
Requirements
MQL4 grammar (particularly about variables, functions and statements)
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 2.2 GB | freecoursewb | 3 years | 2 | 0 | |
| 3 GB | freecoursewb | 3 years | 0 | 0 | |
| 2.7 GB | freecoursewb | 4 years | 0 | 0 | |
|
Udemy - MQL4 Programming for Traders: Build Robust Trading Robots! [Course Drive] Posted by
coursedrive in Other
|
2.2 GB | coursedrive | 5 years | 0 | 2 |
|
Udemy - MQL4 Programming for Traders Build Robust Trading Robots! [GC] Posted by
escobar623 in Other
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2.2 GB | escobar623 | 6 years | 0 | 1 |
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