数学:Mathematics

Protected: Advanced online learning (3) Application to deep learning (mini-batch stochastic gradient descent, momentum method, accelerated gradient method)

Improving computational efficiency by applying mini-batch stochastic gradient descent, momentum, and accelerated gradient methods to deep learning for digital transformation , artificial intelligence , and machine learning tasks.

2022.01.12

オンライン学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics

Protected: Advanced Online Learning (2) Distributed Parallel Processing(Parallelized mini-batch stochastic gradient method, IPM, BSP, SSP)

Distributed parallel processing of online learning (parallelized mini-batch stochastic gradient method, IPM, BSP, SSP) to efficiently process large scale data for digital transformation , artificial intelligence , and machine learning tasks.

2022.01.11

オンライン学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Machine Learning Professional Series “Online Prediction” Reading Memo

Online prediction, which is machine learning that combines prediction and decision making problems used in digital transformation , artificial intelligence , and machine learning tasks.

2022.01.09

オンライン学習強化学習最適化:Optimization確率・統計:Probability and Statistics

Machine Learning Professional Series “Online Machine Learning” Reading Memo

Online learning reference books used for digital transformation , artificial intelligence , and machine learning tasks such as sequential processing of large-scale data.

2022.01.08

微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning

Protected: Advanced online learning (1) High accuracy Approach (Perceptron, PA, PA-I, PA-II, CW, AROW, SCW)

Introduction to various methods for improving the accuracy of online learning for digital transformation , artificial intelligence and machine learning tasks (Perceptron, PA, CW, AROW, SCW)

2022.01.07

オンライン学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

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.

2022.01.06

オンライン学習微分積分:Calculus最適化:Optimization確率・統計:Probability and Statistics線形代数:Linear Algebra

Online learning and online prediction

Online learning is a sequential machine learning technique used in digital transformation , artificial intelligence , and machine learning tasks, and online prediction combines these techniques with decision-making problems.

2022.01.05

オンライン学習強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Implementation in online learning – sparse vector computation and averaging perceptron, averaged stochastic gradient descent, lazy update

Various implementation techniques for online learning for digital transformation , artificial intelligence and machine learning tasks (sparse vector computation and averaging perceptrons, averaged stochastic gradient descent, lazy update).

2022.01.05

オンライン学習最適化:Optimization

Protected: Instance recognition and retrieval (2) General image retrieval

Search optimization using tree structure, hashing, sequential quantization, spectral hashing, k-means hashing, etc. for digital transformation and artificial intelligence tasks, and evaluation using mAP and recall@R.

2022.01.02

データベース技術:DataBase Technology幾何学:Geometry微分積分:Calculus検索技術:Search Technology機械学習:Machine Learning

Rule base, knowledge base, expert system and relational data

Rule-based and knowledge-based and expert systems and relational data as knowledge information processing handling for use in digital transformation and artificial intelligence tasks.

2022.01.01

Symbolic Logicデータベース技術:DataBase Technology数理論理学:Mathematical logic検索技術:Search Technology