線形代数:Linear Algebra

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.
アルゴリズム:Algorithms

Protected: Support Vector Machines – Overview

Overview of SVMs (Support Vector Machines), the basis for various machine learning methods such as classification, regression, and unsupervised learning.
幾何学:Geometry

Fundamentals of Computer Mathematics

Overview of computer mathematics as a basis for artificial intelligence and machine learning techniques, functions, sets, probability, simultaneous equations, differentiation, and integration.
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