オンライン学習 Protected: Trade-off between exploration and utilization -Regret and stochastic optimal measures, heuristics
Reinforcement learning with regrets, stochastic optimal measures, and heuristics
機械学習:Machine Learning
オンライン学習 IOT技術:IOT Technology Time series data analysis
Time Series Data Learning
Overview
Time-series data is called data whose values change over time, such ...
オンライン学習 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
推論技術:inference Technology Statistical Causal Inference and Causal Search
Statistical Causal Inference and Causal Search
Overview
When using machine learning, it is important to consider...
強化学習 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.
最適化:Optimization 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.
オンライン学習 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.
オンライン学習 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.
オンライン学習 Protected: Advanced online learning (3) Application to deep learning (mini-batch stochastic gradient descent, momentum method, accelerated gradient method)
Improving computational efficiency by applying mini-batch stochastic gradient descent, momentum, and accelerated gradient methods to deep learning for digital transformation , artificial intelligence , and machine learning tasks.
オンライン学習 Protected: Advanced Online Learning (2) Distributed Parallel Processing(Parallelized mini-batch stochastic gradient method, IPM, BSP, SSP)
Distributed parallel processing of online learning (parallelized mini-batch stochastic gradient method, IPM, BSP, SSP) to efficiently process large scale data for digital transformation , artificial intelligence , and machine learning tasks.