微分積分:Calculus Machine Learning Professional Series “Continuous Optimization for Machine Learning” Reading Memo
Summary
Continuous optimization in machine learning is a method for solving optimization problems in which varia...
微分積分:Calculus
微分積分:Calculus IOT技術:IOT Technology 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.
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
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
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.
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.
微分積分:Calculus Machine Learning Professional Series “Online Machine Learning” Reading Memo
Online learning reference books used for digital transformation , artificial intelligence , and machine learning tasks such as sequential processing of large-scale data.