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[COURSERA] PRACTICAL REINFORCEMENT LEARNING [FCO]

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[COURSERA] PRACTICAL REINFORCEMENT LEARNING [FCO] (Size: 1.4 GB)
  001. Why should you care.mp4 32.4 MB
  001. Why should you care.srt 15.4 KB
  002. Reinforcement learning vs all.mp4 10.8 MB
  002. Reinforcement learning vs all.srt 4.9 KB
  003. Multi-armed bandit.mp4 17.9 MB
  003. Multi-armed bandit.srt 7.3 KB
  004. Decision process & applications.mp4 23 MB
  004. Decision process & applications.srt 9.7 KB
  005. Markov Decision Process.mp4 18 MB
  005. Markov Decision Process.srt 8.3 KB
  006. Crossentropy method.mp4 36 MB
  006. Crossentropy method.srt 15.5 KB
  007. Approximate crossentropy method.mp4 19.3 MB
  007. Approximate crossentropy method.srt 8.2 KB
  008. More on approximate crossentropy method.mp4 22.9 MB
  008. More on approximate crossentropy method.srt 10.5 KB
  009. Evolution strategies core idea.mp4 20.9 MB
  009. Evolution strategies core idea.srt 7.3 KB
  010. Evolution strategies math problems.mp4 17.7 MB
  010. Evolution strategies math problems.srt 8.6 KB
  011. Evolution strategies log-derivative trick.mp4 27.8 MB
  011. Evolution strategies log-derivative trick.srt 12.6 KB
  012. Evolution strategies duct tape.mp4 21.2 MB
  012. Evolution strategies duct tape.srt 9.7 KB
  013. Blackbox optimization drawbacks.mp4 15.2 MB
  013. Blackbox optimization drawbacks.srt 7.3 KB
  014. Reward design.mp4 49.7 MB
  014. Reward design.srt 23.2 KB
  015. State and Action Value Functions.mp4 37.3 MB
  015. State and Action Value Functions.srt 18.2 KB
  016. Measuring Policy Optimality.mp4 18.1 MB
  016. Measuring Policy Optimality.srt 8.5 KB
  017. Policy evaluation & improvement.mp4 31.9 MB
  017. Policy evaluation & improvement.srt 14.5 KB
  018. Policy and value iteration.mp4 24.2 MB
  018. Policy and value iteration.srt 12.1 KB
  019. Model-based vs model-free.mp4 28.8 MB
  019. Model-based vs model-free.srt 14.1 KB
  020. Monte-Carlo & Temporal Difference; Q-learning.mp4 30.1 MB
  020. Monte-Carlo & Temporal Difference; Q-learning.srt 14.5 KB
  021. Exploration vs Exploitation.mp4 28.2 MB
  021. Exploration vs Exploitation.srt 14 KB
  022. Footnote Monte-Carlo vs Temporal Difference.mp4 10.3 MB
  022. Footnote Monte-Carlo vs Temporal Difference.srt 4.8 KB
  023. Accounting for exploration. Expected Value SARSA..mp4 37.7 MB
  023. Accounting for exploration. Expected Value SARSA..srt 17.3 KB
  024. On-policy vs off-policy; Experience replay.mp4 26.7 MB
  024. On-policy vs off-policy; Experience replay.srt 11.2 KB
  025. Supervised & Reinforcement Learning.mp4 50.6 MB
  025. Supervised & Reinforcement Learning.srt 25.4 KB
  026. Loss functions in value based RL.mp4 33.8 MB
  026. Loss functions in value based RL.srt 15.2 KB
  027. Difficulties with Approximate Methods.mp4 47 MB
  027. Difficulties with Approximate Methods.srt 21.9 KB
  028. DQN bird's eye view.mp4 27.8 MB
  028. DQN bird's eye view.srt 11.4 KB
  029. DQN the internals.mp4 29.6 MB
  029. DQN the internals.srt 12.3 KB
  030. DQN statistical issues.mp4 19.2 MB
  030. DQN statistical issues.srt 9.2 KB
  031. Double Q-learning.mp4 20.5 MB
  031. Double Q-learning.srt 9.4 KB
  032. More DQN tricks.mp4 33.9 MB
  032. More DQN tricks.srt 16.4 KB
  033. Partial observability.mp4 57.2 MB
  033. Partial observability.srt 27.7 KB
  034. Intuition.mp4 34.9 MB
  034. Intuition.srt 15.6 KB
  035. All Kinds of Policies.mp4 16 MB
  035. All Kinds of Policies.srt 7.4 KB
  036. Policy gradient formalism.mp4 31.6 MB
  036. Policy gradient formalism.srt 13.3 KB
  037. The log-derivative trick.mp4 13.3 MB
  037. The log-derivative trick.srt 5.9 KB
  038. REINFORCE.mp4 31.4 MB
  038. REINFORCE.srt 14 KB
  039. Advantage actor-critic.mp4 24.6 MB
  039. Advantage actor-critic.srt 11.8 KB
  040. Duct tape zone.mp4 17.5 MB
  040. Duct tape zone.srt 7.8 KB
  041. Policy-based vs Value-based.mp4 16.8 MB
  041. Policy-based vs Value-based.srt 7.1 KB
  042. Case study A3C.mp4 26.1 MB
  042. Case study A3C.srt 11.1 KB
  043. A3C case study (2 2).mp4 15 MB
  043. A3C case study (2 2).srt 6 KB
  044. Combining supervised & reinforcement learning.mp4 24 MB
  044. Combining supervised & reinforcement learning.srt 11.9 KB
  045. Recap bandits.mp4 24.7 MB
  045. Recap bandits.srt 11.9 KB
  046. Regret measuring the quality of exploration.mp4 21.3 MB
  046. Regret measuring the quality of exploration.srt 10.2 KB
  047. The message just repeats. 'Regret, Regret, Regret.'.mp4 18.4 MB
  047. The message just repeats. 'Regret, Regret, Regret.'.srt 8.7 KB
  048. Intuitive explanation.mp4 22.3 MB
  048. Intuitive explanation.srt 10.9 KB
  049. Thompson Sampling.mp4 17.1 MB
  049. Thompson Sampling.srt 7.9 KB
  050. Optimism in face of uncertainty.mp4 16.5 MB
  050. Optimism in face of uncertainty.srt 7.9 KB
  051. UCB-1.mp4 22.2 MB
  051. UCB-1.srt 10.4 KB
  052. Bayesian UCB.mp4 40.8 MB
  052. Bayesian UCB.srt 19.3 KB
  053. Introduction to planning.mp4 51.6 MB
  053. Introduction to planning.srt 25.4 KB
  054. Monte Carlo Tree Search.mp4 30.9 MB
  054. Monte Carlo Tree Search.srt 14.8 KB
  [FTU Forum].url 204.8 B
  [FreeCoursesOnline.Me].url 102.4 B
  [FreeTutorials.Us].url 102.4 B
  ▲ 111 total files

Description


[COURSERA] PRACTICAL REINFORCEMENT LEARNING [FCO]

About this course: Welcome to the Reinforcement Learning course. Here you will find out about: – foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. — with math & batteries included – using deep neural networks for RL tasks — also known as “the hype train” – state of the art RL algorithms — and how to apply duct tape to them for practical problems. – and, of course, teaching your neural network to play games — because that’s what everyone thinks RL is about. We’ll also use it for seq2seq and contextual bandits. Jump in. It’s gonna be fun!

For more Coursera and other Courses >>> https://www.freecoursesonline.me/
For More Udemy Free Courses >>> http://www.freetutorials.us

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