数学:Mathematics

Protected: Basic concept of anomaly and change detection – Neyman-Pearson Decision Rule

An Introduction to Machine Learning for Anomaly and Change Detection Used in Digital Transformation and Artificial Intelligence Tasks

2021.12.05

最適化:Optimization機械学習:Machine Learning異常検知・変化検知確率・統計:Probability and Statistics

Protected: Applications of Markov chain Monte Carlo methods (Ising, combinatorial optimization, particle physics)

Examples of Markov Chain Monte Carlo (MCMC) applications in digital transformation , artificial intelligence, and machine learning tasks, such as Ising, combinatorial optimization (traveling salesman problem), and particle physics, are discussed.

2021.12.04

機械学習:Machine Learning確率・統計:Probability and Statistics

What is a Complex Network? A New Approach to Deciphering Complex Relationships Reading Memo

Overview of graph theory for analyzing complex network information used in artificial intelligence tasks (lattices and networks, Bacon and Erdesh numbers, small worlds, Beki rules, contagion transmission pathways, communication networks, neural networks, community networks).

2021.12.02

グラフ理論

Protected: Applications of Markov chain Monte Carlo methods (Bayesian inference)

Overview of the application of MCMC methods to Bayesian inference for digital transformation , artificial intelligence , and machine learning tasks, and description of various algorithms

2021.12.02

C言語機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

Protected: MCMC method for calculating stochastic integrals: Algorithms other than Metropolis method (Gibbs sampling, MH method)

An overview of MCMC using Gibbs sampling and MH methods for probability integral computation for digital transformation and artificial intelligence task applications.

2021.11.30

微分積分:Calculus機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

Protected: MCMC method for calculating stochastic integrals: Algorithms other than Metropolis method (HMC method)

Algorithm and C implementation of the Hybrid Monte Calro method applied to complex stochastic integral calculations for digital transformation and artificial intelligence tasks.

2021.11.29

C言語深層学習:Deep Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

Machine Learning Professional Series: Topic Models Post-Reading Notes

Topic models using probability generation models to extract sentence topics to be used in digital transformation (DX) and artificial intelligence (AI) tasks.

2021.11.28

微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: MCMC Method for Stochastic Integral Calculations: Multivariate Metropolis Algorithm

MCMC Method for Stochastic Integral Computation for Digital Transformation and Artificial Intelligence Tasks: The Multivariate Metropolis Algorithm

2021.11.27

C言語機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

Protected: A concrete algorithm for Markov chain Monte Carlo: Metropolis method (2) application and efficiency

An Overview of MCMC Efficiency Using Metropolis Method for Stochastic Integral Computation for Digital Trasformation and Artificial Intelligence Tasks

2021.11.26

C言語機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

Protected: A concrete algorithm for Markov chain Monte Carlo: Metropolis method (1)Overview

Overview of the Metropolis method in MCMC methods used for probability integration and other aspects of machine learning for digital transformation and artificial intelligence tasks.

2021.11.25

C言語機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing