UdacityBecome a Deep Reinforcement Learning Expert v1 0 0

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Posted on 03 Dec 09:41 | by AD-TEAM | 14 views

Udacity-Become a Deep Reinforcement Learning Expert v1.0.0
Language: English
Files Type: mp4, css, gif, ttf, vtt, js, woff2, html, woff| Size: 2.22 GB
Video: 11:47:48 | 1280X720 | 787 Kbps
Audio: mp4a-40-2 | 192 Kbps | AAC
Genre:eLearning

Videos Files :
01. Welcome To DRLND-i1-l0n1ntes.mp4 (8.4 MB)
02. RL In The Real World-IGlAyGbOTHo.mp4 (5.31 MB)
04. Unity Machine Learning Agents-jC12h4UAxqs.mp4 (16.5 MB)
01. Introduction-6jSFl5kxIBs.mp4 (5.15 MB)
02. Applications-CV6B84mKRNM.mp4 (8.46 MB)
03. The Setting-nh8Gwdu19nc.mp4 (7.75 MB)
04. Resources- YPqfAnCqtk.mp4 (6.97 MB)
01. Introduction-X 9l ZqXXBA.mp4 (2.9 MB)
02. The Setting, Revisited-V6Q1uF8a6kA.mp4 (7.36 MB)
03. Episodic vs. Continuing Tasks-E1I-BPanSM8.mp4 (10.07 MB)
06. The Reward Hypothesis-uAqNwgZ49JE.mp4 (4.38 MB)
07. Goals and Rewards, Part 1-XPnj3Ya3EuM.mp4 (6.84 MB)
08. Goals and Rewards, Part 2-pVIFc72VYH8.mp4 (8.05 MB)
10. Cumulative Reward-ysriH65lV9o.mp4 (9.96 MB)
11. Discounted Return-opXGNPwwn7g.mp4 (14.3 MB)
13. MDPs, Part 1-NBWbluSbxPg.mp4 (3.86 MB)
14. MDPs, Part 2-CUTtQvxKkNw.mp4 (6.82 MB)
17. MDPs, Part 3-UlXHFbla3QI.mp4 (14.75 MB)
01. Introduction-9Wyf5Zsska8.mp4 (5.39 MB)
02. Policies-hc3LrvaC13U.mp4 (20.24 MB)
04. Gridworld Example-XeHBmPFqTsE.mp4 (2.38 MB)
05. State-Value Functions-llakAjwox 8.mp4 (5.28 MB)
06. Bellman Equations-UgIaDMvSdUo.mp4 (4.14 MB)
08. Optimality-j231aRV74QM.mp4 (5.99 MB)
09. Action-Value Functions-KJLaRfOOPGA.mp4 (6.6 MB)
11. Optimal Policies-2rguYpVyCto.mp4 (7.11 MB)
01. L601 Intro RENDER V2-3H5x0lstvmo.mp4 (1.18 MB)
02. L602 Gridworld Example RENDER V2-2-Lwibg IfmrA.mp4 (3.46 MB)
03. L603 Monte Carlo Methods RENDER V3-2-titaMCRl224.mp4 (8.29 MB)
04. L604 MC Prediction Part 1RENDER V2-6ts9gdIS6vg.mp4 (4.76 MB)
05. L605 MC Prediction Part 2 RENDER V3-jR49ZyKuJ98.mp4 (1.96 MB)
06. L606 MC Prediction Part 3 RENDERv1 V4-9LP6uXdmWxQ.mp4 (2.05 MB)
09. MC Prediction - Solution Walkthrough-Pwiqk7Pncgc.mp4 (13.73 MB)
11. L611 Greedy Policies RENDER V4-DH6c-aODMLU.mp4 (2.56 MB)
12. L612 Epsilon Greedy Policies RENDER V4-PxJMtlR06MY.mp4 (4.68 MB)
15. L615 Incremental Mean RENDER V4-h-8MB7V1LiE.mp4 (3.32 MB)
16. L617 Constant Alpha Edits RENDER V1-LetHoOtNdJc.mp4 (1.05 MB)
16. MC Control Constant-alpha-QFV1nI9Zpoo.mp4 (12.46 MB)
17. M1 L6 S2 V1-6E 3NJcoxmU.mp4 (13.76 MB)
01. Introduction-yXErXQulI o.mp4 (20.67 MB)
03. L602 Gridworld Example RENDER V2-2-Lwibg IfmrA.mp4 (3.46 MB)
03. Quiz MC Control Methods-ZwIg6LDMyuo.mp4 (2.61 MB)
04. TD Control Sarsa Part 1-HYV0SP9wm7g.mp4 (3.46 MB)
04. TD Control Sarsa Part 2-U CV-UC9G2c.mp4 (2.22 MB)
06. TD Control Sarsamax-4DxoYuR7aZ4.mp4 (16.53 MB)
08. TD Control Expected Sarsa-kEKupCyU0P0.mp4 (4.28 MB)
02. Introduction-GPjK124RU5g.mp4 (33.2 MB)
03. Discrete vs. Continuous Spaces-uHstLeRzaE8.mp4 (21.37 MB)
05. Discretization-j2eZyUpy--E.mp4 (12.55 MB)
08. Tile Coding-BRs7AnTZ 8k.mp4 (11.03 MB)
11. Coarse Coding-Uu1J5KLAfTU.mp4 (10.3 MB)
12. Function Approximation-UTGWVY6jEdg.mp4 (20.08 MB)
13. Linear Function Approximation-OJ5wrB7o-pI.mp4 (28.67 MB)
14. Kernel Functions-RdkPVYyVOvU.mp4 (8.91 MB)
15. Non-Linear Function Approximation-rITnmpD2mN8.mp4 (4.95 MB)
16. Summary-MTEBk43oByU.mp4 (9.91 MB)
01. Arpan Rollercoaster-Rf6cCYRqV58.mp4 (7.81 MB)
02. Deep RL in Robotics-IjG IWJdb1w.mp4 (7.29 MB)
01. DQN Overview-WgiAvr7COR0.mp4 (4.85 MB)
01. From RL to Deep RL-7HLJ0uaR1F0.mp4 (3.54 MB)
02. Deep Q-Networks-GgtR d1OB-M.mp4 (25.67 MB)
03. Experience Replay-wX -SZG-YMQ.mp4 (48.38 MB)
04. Fixed Q-Targets-SWpyiEezfp4.mp4 (20.97 MB)
05. Deep Q-Learning Algorithm-MqTXoCxQ eY.mp4 (17.45 MB)
09. 10 Double DQN V2-PGCEMLujiGI.mp4 (3.46 MB)
10. 10 Prioritized Experience Replay V1-cN8z-7Ze9L8.mp4 (5.86 MB)
11. 10 Dueling DQN V2-zZeHbPs39Ls.mp4 (1.64 MB)
13. Summary-x6JggcDTcys.mp4 (7.2 MB)
01. 01 Introduction RENDER V2-dfeawuScC7k.mp4 (4.65 MB)
02. 02 Welcome!-1oElWzRt-lU.mp4 (7.16 MB)
02. 03 Transitioning-BvDvxw8e0CY.mp4 (1.48 MB)
03. 03 CC API HSSC HS RENDER V3-a9-HdpCaYW4.mp4 (3.3 MB)
09. 09 Jetson TX2 Edits V1-M26z7vTti g.mp4 (6.14 MB)
09. Jetson Overview-i56qM6NNW9A.mp4 (14.64 MB)
10. 10 Summary HS V3-cb1FGgZIitc.mp4 (2.57 MB)
04. Getting Started-ltz2GhFv04A.mp4 (1.58 MB)
02. Career Services-cuKecPpZ7PM.mp4 (10.12 MB)
02. Introduction-Vnj2VNQROtI.mp4 (9.59 MB)
03. GitHub profile important items-prvPVTjVkwQ.mp4 (3.36 MB)
04. Good GitHub repository-qBi8Q1EJdfQ.mp4 (3.72 MB)
05. Interview with Art - Part 1-ClLYamtaO-Q.mp4 (21.79 MB)
06. Identify fixes for example "bad" profile-AF07y1oAim0.mp4 (569.35 KB)
06. Identify fixes for example "bad" profile-ncFtwW5urHk.mp4 (1.59 MB)
07. Quick Fixes-Lb9e2KemR6I.mp4 (3.99 MB)
08. Quick Fixes #2-It6AEuSDQw0.mp4 (2.25 MB)
10. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4 (13.17 MB)
12. Reflect on your commit messages- 0AHmKkfjTo.mp4 (3.03 MB)
13. Participating in open source projects-OxL-gMTizUA.mp4 (2.77 MB)
14. Interview with Art - Part 3-M6PKr3S1rPg.mp4 (25.04 MB)
15. Participating in open source projects 2-elZCLxVvJrY.mp4 (3.3 MB)
16. Starring interesting repositories-U3FUxkm1MxI.mp4 (2.45 MB)
16. Starring interesting repositories-ZwMY5rAAd7Q.mp4 (3.46 MB)
01. M3 L2 C01 V2-mMnhi8yzwKk.mp4 (4.23 MB)
02. M3 L2 C02 V1-v8tGjlc2aG4.mp4 (2.69 MB)
04. M3 L2 C04 V3-5E86a0OyVyI.mp4 (4.97 MB)
05. M3 L2 C05 V1-0XzzqIXyax0.mp4 (1.5 MB)
06. M2L3 04 V1-QicxmyE5vTo.mp4 (21.03 MB)
07. M3 L2 C07 V3-2poDljPvY58.mp4 (2.07 MB)
11. M2L3 02 V2-ToS8vXGdODE.mp4 (32.51 MB)
01. M3L3 C01 V3-ZEhQRASU5O4.mp4 (7.4 MB)
02. M3L3 C02 V6-zoOgRoaxGiU.mp4 (6.51 MB)
03. M3L3 C03 V2-dJz p4FKE-g.mp4 (6.52 MB)
04. M3L3 C04 V2-St9ftvMQ ks.mp4 (4.26 MB)
05. M3L3 C05 V2-o6CI2q3IXEs.mp4 (9.15 MB)
01. Instructor Introduction-sokSgNtGj9Y.mp4 (3.24 MB)
02. Training an agent to play atari-pong!-w27mvWFBnvQ.mp4 (337.4 KB)
04. Noise Reduction-GCGqT2knFJ0.mp4 (2.93 MB)
05. Credit Assignment-tfZw1moB25Y.mp4 (1.7 MB)
07. pong with REINFORCE walkthrough-eKIjPrQWIgg.mp4 (17.37 MB)
09. Importance Sampling-cYPvWriOPIk.mp4 (5.69 MB)
10. PPO Part 1 The Surrogate Function-Y-boYZlpO7g.mp4 (3.27 MB)
11. PPO Part 2 Clipping Policy Updates-NRzjGGX6c34.mp4 (5.27 MB)
12. TLPPO Summary V1-qRAUAAWA kc.mp4 (1.23 MB)
13. Pong with PPO walkthrough-XhfhR7Z01S0.mp4 (8.34 MB)
01. M3L501 Introduction HS 1 V1- OHo1pEaJcQ.mp4 (8.05 MB)
02. M3 L5 02 Motivation V1-dpFPlDtdxyQ.mp4 (6.46 MB)
03. M3 L5 03 Bias And Variance V2- vnkkwm46uU.mp4 (5.69 MB)
04. M3 L5 04 Two Ways For Estimating Expected Returns V3-2W6yIBDvfsQ.mp4 (5.84 MB)
05. M3 L5 05 Baselines And Critics V1-wqmqoiUuQHI.mp4 (14.04 MB)
06. M3 L5 06 Policybased Valuebased And ActorCritic V1-iyin896PNEc.mp4 (16.85 MB)
07. M3 L5 07 A Basic ActorCritic Agent V2-KdHQ24hBKho.mp4 (3.95 MB)
08. M3 L5 08 A3C Asynchronous Advantage ActorCritic V2-twNXFplIAP8.mp4 (14.97 MB)
09. M3 L5 09 A3C Asynchronous Advantage ActorCritic Parallel Training V2-kKRbAKhjACo.mp4 (3.77 MB)
10. M3 L5 10 A3C Asynchronous Advantage ActorCritic Offpolicy Vs Onpolicy V1-AZiy5R0DESU.mp4 (23.31 MB)
11. M3 L5 11 A2C Advantage ActorCritic V2-fIWe3xA97DA.mp4 (3.62 MB)
12. A2c Export V1-LiUBJje2N0c.mp4 (38.55 MB)
13. M3 L5 13 GAE Generalized Advantage Estimation V2-oLFocWp0dt0.mp4 (11.33 MB)
14. M3 L5 14 DDPG Deep Deterministic Policy Gradient Continuous Actionspace V1-0NVOPIyrr98.mp4 (7.85 MB)
15. M3 L5 15 DDPG Deep Deterministic Policy Gradient Soft Updates V1-RT-HDnAVe9o.mp4 (6.62 MB)
16. DDPG Export V1-08V9r3NgFSE.mp4 (30.37 MB)
17. M3L517 Summary HS 1 V1-rRuiMhijw s.mp4 (8.09 MB)
01. M3L601 Introduction HS V1-Nn1PblFSnP8.mp4 (2.99 MB)
02. M3L602 High Frequency Trading HFT RENDER V2-oM1zZdZ-8fE.mp4 (9.6 MB)
03. M3L603 Challenges Of Supervised Learning RENDER V1- hAPnbDtteM.mp4 (9.82 MB)
04. M3L04 Advantages Of Reinforcemnt Learning For Trading RENDER V1-rqHL4BZocI8.mp4 (10.36 MB)
05. M3L606 Optimization SC PT1 V1-6NiRtFyA2DU.mp4 (1.83 MB)
06. M3L607 Optimization SC PT2 V1-JzL66ZbTC9U.mp4 (2.11 MB)
07. M3L608 Optimization SC PT3 V1-3pN77gMg788.mp4 (2.97 MB)
08. M3L609 Optimization SC PT4 V2-N2LP-wg1jEI.mp4 (7.44 MB)
09. M3L610 Almgren And Chriss Model SC V1-rokcEQ4LXbU.mp4 (8.65 MB)
10. M3L611 Trading Lists SC V1-cGT-ADpHR74.mp4 (9.04 MB)
11. M3L612 The Efficient Frontier V1-EwM7Ksbs-ds.mp4 (8.25 MB)
04. Untitled-i2gVvXgOMnc.mp4 (2.16 MB)
01. Why Network-exjEm9Paszk.mp4 (17.37 MB)
02. Meet Chris-0ccflD9x5WU.mp4 (32.54 MB)
03. Elevator Pitch-S-nAHPrkQrQ.mp4 (20.63 MB)
04. Elevator Pitch-0QtgTG49E9I.mp4 (9.98 MB)
04. Pitching to a Recruiter-LxAdWaA-qTQ.mp4 (8.93 MB)
01. M4 L2 C01 Introducing Chhavi HS V1-imuw8tOMed4.mp4 (774.91 KB)
02. M4 L2 C02 Introduction To Multi Agent Systems V1-ra-w63kzq6I.mp4 (4 MB)
03. M4 L2 C03 Motivation For Multi Agent Systems V1-i s22qgQYL4.mp4 (4.61 MB)
04. M4 L2 C04 Applications Of Multi Agent Systems V2-fw0G gSDm6Q.mp4 (2.73 MB)
05. M4 L2 C05 Benefits Of Multi Agent Systems V2-NXDv9cEZTaw.mp4 (4.75 MB)
06. M4 L2 C06 Markov Games 2 V1-Y9qq4Jqnwls.mp4 (4.65 MB)
08. M4 L2 C07 Approaches To MARL V1-uKV9AJykin0.mp4 (5.06 MB)
09. M4 L2 C08 Cooperation Competition Mixed Environments A V1-vx6PIH5 oFg.mp4 (5.87 MB)
10. M4 L2 C09 Paper Description Part I HSAEG V1-nRKrQamUISs.mp4 (2.27 MB)
11. M4 L2 C10a Paper Description Part II V1-Ks9-TeCg3Fs.mp4 (2.89 MB)
12. M4 L2 C10b Paper Description Part II V2-4hFAhtLJR5U.mp4 (2.58 MB)
13. M4 L2 C11 Summary HS V1-yGPHGYHqjq8.mp4 (4.2 MB)
01. Alpha Zero Preview-Zzc1XJ1aJ-4.mp4 (6.76 MB)
02. Zero-Sum Game-uPw1dHVqdXQ.mp4 (4.86 MB)
03. Monte Carlo Tree Search 1 - Random Sampling-wn2B3j Qz6E.mp4 (6.31 MB)
04. Monte Carlo Tree Search 2 - Expansion and Back-propagation-H34Wtk1iNDY.mp4 (6.78 MB)
05. AlphaZero 1 Guided Tree Search-LinuRy47xbw.mp4 (8.02 MB)
06. Alpha Zero 2 Self-Play Training-wl1qfPXqRuQ.mp4 (5.14 MB)
07. Alphazero python classes walkthrough-hKnBQvtJ zQ.mp4 (26.9 MB)
07. TicTacToe using AlphaZero - notebook walkthrough-uUFuBscf98I.mp4 (29.33 MB)
09. Alphazero advanced tictactoe walkthrough-MOIk BbCjRw.mp4 (14.23 MB)
03. Untitled-kxDvrkg8ep0.mp4 (1.48 MB)
01. Introduction-ek2PD9RDrWw.mp4 (6.18 MB)
04. Another Gridworld Example-n9SbomnLb-U.mp4 (4.69 MB)
05. An Iterative Method-AX-hG3KvwzY.mp4 (27.57 MB)
08. Iterative Policy Evaluation-eDXIL oOJHI.mp4 (26.59 MB)
14. Policy Improvement-4 adUEK0IHg.mp4 (30.38 MB)
17. Policy Iteration-gqv7o1kBDc0.mp4 (8.14 MB)
20. Truncated Policy Iteration-a-RvCxlPMho.mp4 (14.13 MB)
23. Value Iteration-XNeQn8N36y8.mp4 (15.65 MB)
02. Why Neural Networks-zAkzOZntK6Y.mp4 (982.27 KB)
03. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4 (2.83 MB)
03. Combinando modelos-Boy3zHVrWB4.mp4 (4.73 MB)
03. Layers-pg99FkXYK0M.mp4 (3.11 MB)
03. Multiclass Classification-uNTtvxwfox0.mp4 (1.88 MB)
04. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4 (5.33 MB)
04. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4 (1.72 MB)
05. Backpropagation V2-1SmY3TZTyUk.mp4 (6.52 MB)
05. Calculating The Gradient 1 -tVuZDbUrzzI.mp4 (3.31 MB)
05. Chain Rule-YAhIBOnbt54.mp4 (1.46 MB)
05. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 (5.69 MB)
06. Training Optimization-UiGKhx9pUYc.mp4 (2.96 MB)
07. Testing-EeBZpb-PSac.mp4 (2 MB)
08. Underfitting And Overfitting-xj4PlXMsN-Y.mp4 (6.42 MB)
09. Model Complexity Graph-NnS0FJyVcDQ.mp4 (4.9 MB)
10. DL 53 Q Regularization-KxROxcRsHL8.mp4 (1.01 MB)
11. Regularization-ndYnUrx8xvs.mp4 (7.57 MB)
12. Dropout-Ty6K6YiGdBs.mp4 (4.22 MB)
13. Local Minima-gF sW nY-xw.mp4 (819.86 KB)
14. Vanishing Gradient-W JJm 5syFw.mp4 (1.32 MB)
15. Other Activation Functions-kA-1vUt6cvQ.mp4 (2.3 MB)
16. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4 (3.95 MB)
17. Learning Rate-TwJ8aSZoh2U.mp4 (927.05 KB)
18. Random Restart-idyBBCzXiqg.mp4 (394.99 KB)
19. Momentum-r-rYz PEWC8.mp4 (2.14 MB)
02. 03 Data And Lesson Outline RENDER V2-jPr-5aZA6NE.mp4 (5.03 MB)
04. Convolutional Layers (Part 2)-LX-yVob3c28.mp4 (19.85 MB)
08. Pooling Layers-OkkIZNs7Cyc.mp4 (5.82 MB)
15. Dropout-Ty6K6YiGdBs.mp4 (4.22 MB)
15. 动量-r-rYz PEWC8.mp4 (2.14 MB)
17. 04 Feature Visualization V1 RENDER V2-xwGa7RFg1EQ.mp4 (4.54 MB)
18. 05 Feature Maps V1RENDER V3-oRhsJHHWtu8.mp4 (2.84 MB)
19. 06 First Convolutional Layer T1 V1 RENDER V2-hIHDMWVSfsM.mp4 (9.51 MB)
21. 10 Visualizing Activations V1 RENDER V2-CJLNTOXqt3I.mp4 (3.35 MB)
26. 20 Summary Of Feature Viz V2 RENDER V2-r2LBoEkXskU.mp4 (3.49 MB)
03. Part 1 V2-n4mbZYIfKb4.mp4 (13.81 MB)
04. Py Part 2 V1-u50 ZyKqt8g.mp4 (34.58 MB)
05. Py Part 3 V2-u8hDj5aJK6I.mp4 (28.37 MB)
06. PyTorch - Part 4-AEJV RKZ7VU.mp4 (3.32 MB)
07. Py Part 5 V2-coBbbrGZXI0.mp4 (27.08 MB)
08. Py Part 6 V1-HiTih59dCWQ.mp4 (15.94 MB)
09. PyTorch - Part 7-hFu7GTfRWks.mp4 (14.62 MB)
10. Py Part 8 V1-3eqn5sgCOsY.mp4 (24.88 MB)
01. Introduction-ahoiVrq4qAk.mp4 (6.03 MB)
02. Lesson Overview C++-lR3PH3bL-9U.mp4 (7.4 MB)
02. Nd113 C3 L1 04 L Lesson Overview 2 V1-DjT2E23xhj8.mp4 (5.85 MB)
04. Why C++- t4ZvwfnuCA.mp4 (11.98 MB)
06. Static Vs Dynamic Typing-D7v6iIAORkE.mp4 (10.49 MB)
10. Doubles Are Bigger-uhwTWgmM2iY.mp4 (5.13 MB)
15. Two Functions Same Name-0ZF649G58l4.mp4 (6.09 MB)
15. Two Functions Same Name-9SgmzOfBmRU.mp4 (13.16 MB)
16. Function Signatures 1-T6kQ 4w98IQ.mp4 (11.65 MB)
17. Function Signatures 2-Sx4AWTmXl2U.mp4 (8.1 MB)
17. Function Signatures 3 V1-U3QAFb3AS1M.mp4 (2.2 MB)
22. Nd113 C Basics Last Video V1-dtu-RXovl0U.mp4 (1.63 MB)
01. Introduction To Compilation-dyzGEB8YDGg.mp4 (7.96 MB)
01. Introduction-4xHI5LFX-cQ.mp4 (4.28 MB)
03. Why Use Object Oriented Programming-G2KzZfNu9Ak.mp4 (10.64 MB)
01. Course Introduction-Lwc5oYApdUM.mp4 (3.25 MB)
02. C Opt 01 L V2-Kdx1 BI5ddc.mp4 (7.76 MB)
03. 02 L Intro To Comp HW V1 RENDER V1-WDMGkq9mkB8.mp4 (4.26 MB)
04. Nd113 Embedded Terminal V1-Bhl5JQ N9V8.mp4 (26.5 MB)
07. 03 L Binary V1 RENDER V1-K6CpHxnhc2s.mp4 (5.37 MB)
10. 04 L C And RAM V1 RENDER V1-60jEbKV1UOI.mp4 (3.58 MB)
12. C Opt 05 L V3-rTtZVyWxYG8.mp4 (4.47 MB)
16. Nd113 Story 1 V1-lIe2zso8A-w.mp4 (12.76 MB)
19. Nd113 C L2 01 V1-h P7ceb5ido.mp4 (2.31 MB)

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