最適化:Optimization

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 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

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.

2022.01.12

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

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.

2022.01.11

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

Machine Learning Professional Series “Online Prediction” Reading Memo

Online prediction, which is machine learning that combines prediction and decision making problems used in digital transformation , artificial intelligence , and machine learning tasks.

2022.01.09

オンライン学習強化学習最適化:Optimization確率・統計:Probability and Statistics

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.

2022.01.08

微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning

Protected: Advanced online learning (1) High accuracy Approach (Perceptron, PA, PA-I, PA-II, CW, AROW, SCW)

Introduction to various methods for improving the accuracy of online learning for digital transformation , artificial intelligence and machine learning tasks (Perceptron, PA, CW, AROW, SCW)

2022.01.07

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

Protected: Fundamentals of Online Learning Stochastic Gradient Descent – Application to Perceptron, SVM, and Logistic Regression

Online learning applications to the perceptron, SVM, and logistic regression for digital transformation , artificial intelligence , and machine learning tasks.

2022.01.06

オンライン学習微分積分:Calculus最適化:Optimization確率・統計:Probability and Statistics線形代数:Linear Algebra