TorBT - Torrents and Magnet Links Search Engine
Latest Searches: hottest, tomb+raider, дети, sw, curved air, harlequin, bitch, jwties, 禁漫, img

[FreeCoursesOnline.Me] Coursera - Practical Reinforcement Learning

Magnet Download Download Torrent
File Name
Size
001.Welcome/001. Why should you care.mp4
32 MB
001.Welcome/001. Why should you care.srt
15 kB
001.Welcome/002. Reinforcement learning vs all.mp4
11 MB
001.Welcome/002. Reinforcement learning vs all.srt
4.9 kB
002.Reinforcement Learning/003. Multi-armed bandit.mp4
18 MB
002.Reinforcement Learning/003. Multi-armed bandit.srt
7.3 kB
002.Reinforcement Learning/004. Decision process & applications.mp4
23 MB
002.Reinforcement Learning/004. Decision process & applications.srt
9.7 kB
003.Black box optimization/005. Markov Decision Process.mp4
18 MB
003.Black box optimization/005. Markov Decision Process.srt
8.3 kB
003.Black box optimization/006. Crossentropy method.mp4
36 MB
003.Black box optimization/006. Crossentropy method.srt
16 kB
003.Black box optimization/007. Approximate crossentropy method.mp4
19 MB
003.Black box optimization/007. Approximate crossentropy method.srt
8.2 kB
003.Black box optimization/008. More on approximate crossentropy method.mp4
23 MB
003.Black box optimization/008. More on approximate crossentropy method.srt
10 kB
004.All the cool stuff that isn't in the base track/009. Evolution strategies core idea.mp4
21 MB
004.All the cool stuff that isn't in the base track/009. Evolution strategies core idea.srt
7.3 kB
004.All the cool stuff that isn't in the base track/010. Evolution strategies math problems.mp4
18 MB
004.All the cool stuff that isn't in the base track/010. Evolution strategies math problems.srt
8.6 kB
004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.mp4
28 MB
004.All the cool stuff that isn't in the base track/011. Evolution strategies log-derivative trick.srt
13 kB
004.All the cool stuff that isn't in the base track/012. Evolution strategies duct tape.mp4
21 MB
004.All the cool stuff that isn't in the base track/012. Evolution strategies duct tape.srt
9.7 kB
004.All the cool stuff that isn't in the base track/013. Blackbox optimization drawbacks.mp4
15 MB
004.All the cool stuff that isn't in the base track/013. Blackbox optimization drawbacks.srt
7.3 kB
005.Striving for reward/014. Reward design.mp4
50 MB
005.Striving for reward/014. Reward design.srt
23 kB
006.Bellman equations/015. State and Action Value Functions.mp4
37 MB
006.Bellman equations/015. State and Action Value Functions.srt
18 kB
006.Bellman equations/016. Measuring Policy Optimality.mp4
18 MB
006.Bellman equations/016. Measuring Policy Optimality.srt
8.5 kB
007.Generalized Policy Iteration/017. Policy evaluation & improvement.mp4
32 MB
007.Generalized Policy Iteration/017. Policy evaluation & improvement.srt
14 kB
007.Generalized Policy Iteration/018. Policy and value iteration.mp4
24 MB
007.Generalized Policy Iteration/018. Policy and value iteration.srt
12 kB
008.Model-free learning/019. Model-based vs model-free.mp4
29 MB
008.Model-free learning/019. Model-based vs model-free.srt
14 kB
008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.mp4
30 MB
008.Model-free learning/020. Monte-Carlo & Temporal Difference; Q-learning.srt
14 kB
008.Model-free learning/021. Exploration vs Exploitation.mp4
28 MB
008.Model-free learning/021. Exploration vs Exploitation.srt
14 kB
008.Model-free learning/022. Footnote Monte-Carlo vs Temporal Difference.mp4
10 MB
008.Model-free learning/022. Footnote Monte-Carlo vs Temporal Difference.srt
4.8 kB
009.On-policy vs off-policy/023. Accounting for exploration. Expected Value SARSA..mp4
38 MB
009.On-policy vs off-policy/023. Accounting for exploration. Expected Value SARSA..srt
17 kB
010.Experience Replay/024. On-policy vs off-policy; Experience replay.mp4
27 MB
010.Experience Replay/024. On-policy vs off-policy; Experience replay.srt
11 kB
011.Limitations of Tabular Methods/025. Supervised & Reinforcement Learning.mp4
51 MB
011.Limitations of Tabular Methods/025. Supervised & Reinforcement Learning.srt
25 kB
011.Limitations of Tabular Methods/026. Loss functions in value based RL.mp4
34 MB
011.Limitations of Tabular Methods/026. Loss functions in value based RL.srt
15 kB
011.Limitations of Tabular Methods/027. Difficulties with Approximate Methods.mp4
47 MB
011.Limitations of Tabular Methods/027. Difficulties with Approximate Methods.srt
22 kB
012.Case Study Deep Q-Network/028. DQN bird's eye view.mp4
28 MB
012.Case Study Deep Q-Network/028. DQN bird's eye view.srt
11 kB
012.Case Study Deep Q-Network/029. DQN the internals.mp4
30 MB
012.Case Study Deep Q-Network/029. DQN the internals.srt
12 kB
013.Honor/030. DQN statistical issues.mp4
19 MB
013.Honor/030. DQN statistical issues.srt
9.2 kB
013.Honor/031. Double Q-learning.mp4
20 MB
013.Honor/031. Double Q-learning.srt
9.4 kB
013.Honor/032. More DQN tricks.mp4
34 MB
013.Honor/032. More DQN tricks.srt
16 kB
013.Honor/033. Partial observability.mp4
57 MB
013.Honor/033. Partial observability.srt
28 kB
014.Policy-based RL vs Value-based RL/034. Intuition.mp4
35 MB
014.Policy-based RL vs Value-based RL/034. Intuition.srt
16 kB
014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.mp4
16 MB
014.Policy-based RL vs Value-based RL/035. All Kinds of Policies.srt
7.4 kB
014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.mp4
32 MB
014.Policy-based RL vs Value-based RL/036. Policy gradient formalism.srt
13 kB
014.Policy-based RL vs Value-based RL/037. The log-derivative trick.mp4
13 MB
014.Policy-based RL vs Value-based RL/037. The log-derivative trick.srt
5.9 kB
015.REINFORCE/038. REINFORCE.mp4
31 MB
015.REINFORCE/038. REINFORCE.srt
14 kB
016.Actor-critic/039. Advantage actor-critic.mp4
25 MB
016.Actor-critic/039. Advantage actor-critic.srt
12 kB
016.Actor-critic/040. Duct tape zone.mp4
18 MB
016.Actor-critic/040. Duct tape zone.srt
7.8 kB
016.Actor-critic/041. Policy-based vs Value-based.mp4
17 MB
016.Actor-critic/041. Policy-based vs Value-based.srt
7.1 kB
016.Actor-critic/042. Case study A3C.mp4
26 MB
016.Actor-critic/042. Case study A3C.srt
11 kB
016.Actor-critic/043. A3C case study (2 2).mp4
15 MB
016.Actor-critic/043. A3C case study (2 2).srt
6.0 kB
016.Actor-critic/044. Combining supervised & reinforcement learning.mp4
24 MB
016.Actor-critic/044. Combining supervised & reinforcement learning.srt
12 kB
017.Measuting exploration/045. Recap bandits.mp4
25 MB
017.Measuting exploration/045. Recap bandits.srt
12 kB
017.Measuting exploration/046. Regret measuring the quality of exploration.mp4
21 MB
017.Measuting exploration/046. Regret measuring the quality of exploration.srt
10 kB
017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.mp4
18 MB
017.Measuting exploration/047. The message just repeats. 'Regret, Regret, Regret.'.srt
8.7 kB
018.Uncertainty-based exploration/048. Intuitive explanation.mp4
22 MB
018.Uncertainty-based exploration/048. Intuitive explanation.srt
11 kB
018.Uncertainty-based exploration/049. Thompson Sampling.mp4
17 MB
018.Uncertainty-based exploration/049. Thompson Sampling.srt
7.9 kB
018.Uncertainty-based exploration/050. Optimism in face of uncertainty.mp4
16 MB
018.Uncertainty-based exploration/050. Optimism in face of uncertainty.srt
7.9 kB
018.Uncertainty-based exploration/051. UCB-1.mp4
22 MB
018.Uncertainty-based exploration/051. UCB-1.srt
10 kB
018.Uncertainty-based exploration/052. Bayesian UCB.mp4
41 MB
018.Uncertainty-based exploration/052. Bayesian UCB.srt
19 kB
019.Planning with Monte Carlo Tree Search/053. Introduction to planning.mp4
52 MB
019.Planning with Monte Carlo Tree Search/053. Introduction to planning.srt
25 kB
019.Planning with Monte Carlo Tree Search/054. Monte Carlo Tree Search.mp4
31 MB
019.Planning with Monte Carlo Tree Search/054. Monte Carlo Tree Search.srt
15 kB
[FreeCoursesOnline.Me].url
133 B
[FreeTutorials.Us].url
119 B
[FTU Forum].url
252 B