確率・統計:Probability and Statistics

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

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

This is a good introduction to deep learning (Machine Learning Startup Series)Reading Notes

Overview of deep learning for digital transformation and artificial intelligence tasks, including machine learning, gradient descent, regularization, error back propagation, self-encoders, convolutional neural networks, recurrent neural networks, Boltzmann machines, and reinforcement learning.

2021.11.15

微分積分:Calculus数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Statistics as a post-modern science

Mathematical statistics and probabilistic statistical methods used in digital transformation and artificial intelligence tasks, and considerations for inference and machine learning

2021.10.25

推論技術:inference Technology数理論理学:Mathematical logic機械学習:Machine Learning確率・統計:Probability and Statistics

Introduction to Optimization Problems Combining Cone Optimization, Integer Optimization, and Network Models Problem Solving with Python Series

Overview of optimization techniques in machine learning using python for digital transformation (DX) and artificial intelligence (AI) tasks.

2021.10.24

python微分積分:Calculus推論技術:inference Technology機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

An introduction to statistical modeling through Python implementation

An overview of machine learning theory using probability for digital transformation and artificial intelligence tasks and its implementation using python

2021.10.24

python推論技術:inference Technology機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Model the living word(Probabilistic Modeling of Natural Language)

Probabilistic models of natural language (Hierarchical Bayes, Topic Models, LDA) for artificial intelligence (AI), natural language processing, and digital transformation (DX)

2021.10.24

推論技術:inference Technology機械学習:Machine Learning確率・統計:Probability and Statistics