R Protected: Hierarchical Bayesian Modeling on a Map
Application of Hierarchical Bayesian Inference to Geospatial Information for Digital Transformation and Artificial Intelligence Tasks (OpenBUGS) and Visualization with mandara
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Deux Ex Machina
R 地理空間情報処理 Protected: Cooperation between R and QGIS
Actual collaboration between QGIS and R (rgdal, raster, dismo, dplyr, deldir), an open source of map spatial information for digital transformation and artificial intelligence task utilization
python Python Basic & Practical Programming
Python's Programming Basics and Algorithm Implementation Reference Book
python A Textbook of Algorithms in Python
Python's Programming Basics and Algorithm Implementation Reference Book
機械学習:Machine Learning Protected: Capturing “Individuality” with Hierarchical Models (Hierarchical Bayesian Models and Empirical Bayesian Methods (GLMM))
Understanding "personality" in hierarchical Bayesian models and solving with empirical Bayesian methods (GLMM), which can be used for artificial intelligence (AI), natural language processing, and digital transformation (DX).
Clojure Reference books on Clojure (general)
Reference books on Clojure (microservices, reactive programming, etc.) for building artificial intelligence and machine learning to be used for digital transformation and artificial intelligence (AI) tasks.
地理空間情報処理 Protected: Introduction to Geospatial Information Using QGIS
Overview of QGIS, an open source for processing and displaying map spatial information used in digital transformation and artificial intelligence tasks
Uncategorized Protected: KFAS and fileds in R implementation
Practice of statistical modeling such as Bayesian estimation and spline completion in R, KFAS, fields
機械学習:Machine Learning Protected: Applying Deep Learning to Speech Recognition
Overview of neural network applications (TDNN, RNN, CNN) and deep learning applications (LSTM, CTC) for speech recognition technology used in digital transformation and artificial intelligence tasks
推論技術:inference Technology Concrete and Abstract – natural language sematics and explain
Concrete and abstract to consider the meaning of natural language and explainable machine learning that can be used for digital transformation and artificial intelligence tasks.