数学:Mathematics

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

Protected: General Theory of MCMC Methods: Applying Markov Chains to Monte Carlo Methods

Application of Markov Chains to Monte Carlo methods for efficient computation of probability/combination and other integrals for digital transformation and artificial intelligence tasks.

2021.11.24

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

Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp Reading Memo

Explanation of basic ideas of various artificial intelligence techniques using LISP (GPS, ELIZA, SUDENT, symbol processing systems, natural language processing systems, Prolog, expert systems, etc.)

2021.11.23

LISPprolog推論技術:inference Technology数理論理学:Mathematical logic

Protected: On probability, expectation and Monte Carlo methods

Explanation of the Monte Carlo method, which is the basis of the Markov Chain Monte Carlo (MCMC) method used in integral calculations for machine learning used in digital transformation and artificial intelligence tasks.

2021.11.22

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

Making Logic Part 4 – Logic is Interesting from Here Non-Classical Logic Reading Notes

Fundamentals of logic (non-classical logic, multi-valued logic, intuitionistic logic, and haphazard logic) in mathematical logic used for reasoning mechanisms in artificial intelligence tasks.

2021.11.21

推論技術:inference Technology数理論理学:Mathematical logic

Creating Logic, Part 3 – Another Look at Logic Reading Notes

Foundations of logic (deductive reasoning, syntax and semantics) in mathematical logic used for reasoning mechanisms in artificial intelligence tasks.

2021.11.20

推論技術:inference Technology数理論理学:Mathematical logic