artificial intelligence

Protected: Model-based reinforcement learning(Sparse sampling, UCT, Monte Carlo search tree)

Model-based reinforcement learning (sparse sampling, UCT, Monte Carlo search trees) used for digital transformation artificial intelligence , and machine learning tasks.

2022.01.24

IOT技術:IOT TechnologyStream Data Processing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: Model-free reinforcement learning (2) – Method iteration (Q-learning, SARSA, Actor-click method)

Value iteration methods Q-learning, SARSA, Actor-critic methods to model-free reinforcement learning for digital transformation , artificial intelligence and machine learning tasks.

2022.01.21

IOT技術:IOT TechnologyStream Data Processing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: Model-free reinforcement learning(1) – Value iteration methods (Monte Carlo, TD, TD(λ))

Application of value iterative methods (Monte Carlo, TD, TD(λ)) to model-free reinforcement learning used in digital transformation , artificial intelligence , and machine learning.

2022.01.20

オンライン学習強化学習推論技術:inference Technology機械学習:Machine Learning

Protected: Trade-off between exploration and utilization -Regret and stochastic optimal measures, heuristics

Reinforcement learning with regrets, stochastic optimal measures, and heuristics

2022.01.19

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: Planning Problems (2) Implementation of Dynamic Programming (Value Iterative Method and Measure Iterative Method)

Implementation of Dynamic Programming (Value Iteration and Policy Iteration) for Planning Problems as Reinforcement Learning for Digital Transformation , Artificial Intelligence and Machine Learning Tasks

2022.01.18

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: Planning Problems(1) – Approaches Using Dynamic Programming and Theoretical Underpinnings

Reinforcement learning by planning problems (dynamic programming and linear programming) for sequential decision problems in known environments used for digital transformation , artificial intelligence and machine learning tasks.

2022.01.17

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

Machine Learning Professional Series – Statistical Causal Search Reading Notes

Statistical causal search to find cause and effect relationships in vast amounts of data used for digital transformation , machine learning , and artificial intelligence tasks.

2022.01.16

Uncategorized

Machine Learning Professional Series Sparsity-Based Machine Learning Reading Notes

Overview of sparse modeling used for regularization and other applications in machine learning for digital transformation , artificial intelligence , and machine learning tasks.

2022.01.15

最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning

Protected: Evaluating the performance of online learning(Perceptron, Regret Analysis, FTL, RFTL)

Perceptron and Riglet Analysis (FTL, RFTL) for evaluating online learning used for digital transformation , artificial intelligence , and machine learning tasks.

2022.01.14

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: Advanced online learning (4) Application to deep learning (AdaGrad, RMSprop, ADADELTA, vSGD)

Application to online learning in AdaGrad, RMSprop, and vSGD used for digital transformation , artificial intelligence , and machine learning tasks.

2022.01.13

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