アルゴリズム:Algorithms Encryption and security technologies and data compression techniques
Encryption, Security and Data Compression Technologies for Digital Transformation (DX) and Artificial Intelligence (AI) Tasks
線形代数:Linear Algebra
アルゴリズム:Algorithms Stream Data Processing Protected: Time Series Data Analysis (1) – State Space Model
Overview of various state-space models linear and Gaussian state-space models, AR models, autoregressive and moving average ARMA models, component decomposition models, and time-varying coefficient models) for time series data analysis used in digital transformation, artificial intelligence, and machine learning tasks
グラフ理論 Structural Learning
Structural Learning
Overview
Learning the structure that data has is important for interpreting what the data ...
微分積分:Calculus Machine Learning Professional Series “Continuous Optimization for Machine Learning” Reading Memo
Summary
Continuous optimization in machine learning is a method for solving optimization problems in which varia...
オンライン学習 Protected: Fundamentals of Online Learning Stochastic Gradient Descent – Application to Perceptron, SVM, and Logistic Regression
Online learning applications to the perceptron, SVM, and logistic regression for digital transformation , artificial intelligence , and machine learning tasks.
微分積分:Calculus 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.
微分積分:Calculus 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.
python 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.
機械学習:Machine Learning Protected: Matrix Decomposition -Extraction of relational features between two objects
Extraction of relationships by machine learning, matrix factorization approach, non-negative matrix factorization
最適化:Optimization Protected: Clustering of symmetric relational data – Spectral clustering
Extraction of relationships, knowledge extraction and prediction, spectral clustering by machine learning for graph analysis, etc.