Symbolic Logic Mathematics in Machine Learning
On the mathematics underlying all computer programming and algorithms, including digital transformation (DX), artificial intelligence (AI), and machine learning (ML)
machine learning
Symbolic Logic 最適化:Optimization Protected: Continuous Optimization in Machine Learning – Overview
Explanation of the mathematical theory underlying optimization algorithms for machine learning.
機械学習:Machine Learning General Machine Learning and Data Analysis
Overview of machine learning for beginners, differences between types of learning and results, no-free lunch theorem as a frame for machine learning.
python Clustering with R – k-means
Machine learning using R, unsupervised, non-hierarchical classification using k-means
R hierarchical clustering with R
Machine learning using R, classification with hierarchical clustering function hclust
R R Language Preferences
Overview of machine learning using R, setting up the environment and handling data
R Reference books on machine learning with R
Overview of machine learning with R, history of R and introduction to reference books
自然言語処理:Natural Language Processing Openrefine: Data cleansing tool
Overview of openrefine, a natural language processing tool used in DX and the Semantic Web
機械学習:Machine Learning Protected: Bayesian Estimation and Information Theory
Bayesian estimation and information theory for artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), from de Moivre's probability theory to Bayesian probability and Shannon's information engineering and tools for Bayesian estimation (STAN)
Clojure Sentence classification using liblinear and natural language processing
Classification in liblinear with Clojure, a natural language processing tool., liblinear, SVM, classification with Clojure