機械学習:Machine Learning

Protected: Online Convex Optimization(1) FTL strategy and BTL supplement

Online Convex Optimization and FTL Strategies with Online Prediction for Digital Transformation , Artificial Intelligence , and Machine Learning Tasks with BTL Supplement

2022.02.08

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: New Developments in Reinforcement Learning (2) – Approaches Using Deep Learning

On seven methods for improving deep reinforcement learning used in digital transformation , artificial intelligence , and machine learning tasks (first generation DQN, dual Q learning (dual DQN method), prioritized experience replay, collision Q networks, distributed reinforcement learning (categorical DQN method) noise networks, n-step cutting returns) and alpha zero

2022.02.07

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics

Zen thought and history, Mahayana Buddhism, Taoist thought, Christianity

　 Zen thought and history, Mahayana Buddhism, Taoist thought, Christianity Zen, derived from the Zen sect of Bud...

2022.02.06

life tips人工知能:Artificial Intelligence機械学習:Machine Learning禅:Zen

Protected: New Developments in Reinforcement Learning (1) – Reinforcement Learning with Risk Indicators

Different approaches (regular process TD learning, RDPS methods) and implementations (Monte Carlo, analytical methods) in risk-aware reinforcement learning methods for digital transformation , artificial intelligence , and machine learning tasks.

2022.02.03

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics

Protected: Partially Observed Markov Decision Processes (2) Planning POMDPs

Reinforcement learning for digital transformation , artificial intelligence , and machine learning tasks; obtaining optimal strategies using partial observation Markov decision process planning methods.

2022.02.02

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics

Protected: Partially Observed Markov Decision Processes (1) On POMDPs and Belief MDPs

Belief MDPs, more flexible reinforcement learning using partially observed Markov decision processes (POMDPs) for digital transformation , artificial intelligence , and machine learning tasks.

2022.02.01

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Reinforcement Learning with Function Approximation (3) – Function Approximation for Policy Functions

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2022.01.31

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics

Protected: Reinforcement Learning with Function Approximation (2) – Function Approximation of Value Functions (For Online Learning)

Theory of function approximation online methods gradient TD learning, least-squares based least-squares TD learning (LSTD), GTD2)for reinforcement learning with a huge number of states used in digital transformation , artificial intelligence , and machine learning tasks, and regularization with LASSO.

2022.01.28

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics

Protected: Reinforcement Learning with Function Approximation (1) – Function Approximation of Value Functions (Batch Learning Case)

Function approximation in the case of batch learning of value functions to deal with a huge number of states in reinforcement learning for digital transformation, artificial intelligence, and machine learning tasks.

2022.01.26

強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Modeling of time series and spatial data (1)(Dynamic linear model)

Bayesian modeling of temporal and spatial models with a focus on dynamic linear models and evaluation using MCMC

2022.01.25

推論技術:inference Technology機械学習:Machine Learning確率・統計:Probability and Statistics