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[FreeCoursesOnline.Me] [Packt] Hands-On Reinforcement Learning with Java [FCO]
magnet:?xt=urn:btih:9ef32795e424381edf66efceddd8df00c5bc4ec9&dn=[FreeCoursesOnline.Me] [Packt] Hands-On Reinforcement Learning with Java [FCO]
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9ef32795e424381edf66efceddd8df00c5bc4ec9
infohash:
24
文件数量
343.64 MB
文件大小
2019-10-20 02:38
创建日期
2024-8-26 19:12
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FreeCoursesOnline
Me
Packt
Hands-On
Reinforcement
Learning
with
Java
FCO
01.Deep Dive into Reinforcement Learning with DL4J RL4J/0101.The Course Overview.mp4 25.35 MB
01.Deep Dive into Reinforcement Learning with DL4J RL4J/0102.Main Principles of Reinforcement Learning.mp4 19.44 MB
01.Deep Dive into Reinforcement Learning with DL4J RL4J/0103.Adding DL4J with RL4J to Our Project.mp4 18.26 MB
01.Deep Dive into Reinforcement Learning with DL4J RL4J/0104.Best Use Cases of Reinforcement Learning.mp4 5.74 MB
01.Deep Dive into Reinforcement Learning with DL4J RL4J/0105.Configuring Reinforcement Learning Model with QLearning.QLConfiguration.mp4 12.89 MB
02.Solving Cartpole with Markov Decision Processes (MDPs)/0201.Understanding Cartpole Problem.mp4 5.38 MB
02.Solving Cartpole with Markov Decision Processes (MDPs)/0202.Leveraging Markov Chain in Our Cartpole Solution.mp4 10.06 MB
02.Solving Cartpole with Markov Decision Processes (MDPs)/0203.Using QLConfiguration to Configure Our Model.mp4 9.45 MB
02.Solving Cartpole with Markov Decision Processes (MDPs)/0204.Using GymEnv Library from RL4J to Simulate Solution.mp4 12.93 MB
02.Solving Cartpole with Markov Decision Processes (MDPs)/0205.Running Cartpole and Validating Results.mp4 20.16 MB
03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0301.Adding Malmo Library to Our RL4J Project.mp4 13.76 MB
03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0302.Analyzing Possible Scenarios That Our Program Can Solve.mp4 3.04 MB
03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0303.Loading Cliff Walking Simulation.mp4 14.03 MB
03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0304.Configuring RL4J Algorithm for Cliff Walking Problem.mp4 23.49 MB
03.Using Project Malmo Reinforcement Learning Leveraging Dynamic Programming/0305.Starting QLearningDiscreteDense and Saving Results.mp4 19.87 MB
04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0401.Understanding Stock Prediction Problem.mp4 5.14 MB
04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0402.Creating Configuration for Stock Prediction Learning.mp4 10.21 MB
04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0403.Leveraging QLearningDiscreteDense from RL4J API.mp4 14.03 MB
04.Creating Decision Process for Stock Prediction with Rewards Using Q-Learning/0404.Performing Stock Prediction Training and Validating Results.mp4 11.99 MB
05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0501.Understanding Asynchronous Advantage Actor-Critic Technique(A3C).mp4 5.97 MB
05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0502.Setting Up A3C Learning Environment.mp4 8.57 MB
05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0503.Configuring Reinforcement Learning Program Using A3C Configuration.mp4 15.39 MB
05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0504.Using A3C Technique with ActorCriticFactorySeparateStdDense.mp4 13.25 MB
05.Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL/0505.Starting Program and Gathering Results.mp4 45.27 MB
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