machine learning

Protected: Online-type stochastic optimization for machine learning with AdaGrad and minimax optimization

Online stochastic optimization and AdaGrad for machine learning utilized in digital transformation, artificial intelligence, and machine learning tasks, minimax optimization sparsity patterns, training errors, batch stochastic optimization, online stochastic optimization, batch gradient method, minimax optimality, generalization error, Lipschitz continuity, strong convexity, minimax optimal error, minimax error evaluation, first-order stochastic oracle, stochastic dual averaging method, stochastic gradient descent method, regular terms, Nemirovsky, Yudin, convex optimization method, expected error bound, riglets, semidefinite matrix, mirror image descent method, soft threshold functions

2022.12.28

アルゴリズム:Algorithms微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Leveraging Apache Spark for Distributed Data Processing – Developing and Executing Applications

Leveraging Apache Spark to enable distributed data processing for digital transformation, artificial intelligence, and machine learning tasks -Application development and execution (forced termination, yarn-client mode, yarn-cluster mode, YARN, and YARN) management commands, cluster, python, Clojure, Shell, AWS, Glue, sparkplug, spark-shell, spark-submit, Nodemanager, HDFS, Spark applications, Scala, sbt, plugin.sbt, build.sbt build.sbt, build, sbt-assembly plugin, JAR file)

2022.12.27

IOT技術:IOT TechnologyStream Data Processingweb技術:web technologyアーキテクチャデータベース技術:DataBase Technologyネットワーク技術ハードウェアマルチエージェントシステム時系列データ解析機械学習:Machine Learning

What is Docker? Advantages and Challenges, Differences from Virtualization Infrastructure and Architecture Overview

Advantages and challenges of Docker used for digital transformation, artificial intelligence, and machine learning tasks, differences from virtualization infrastructure and architecture overview cgroups, resource management tools, virtual files, Linux kernel, Windows, Windows Server, pid namespace, user namespace, uts namespace, net namespace, nmt namespace, ipc namespace, hypervisor type, namespace, virtualization software, WIndows Server Container, non-stop server, mission-critical Live Migration, Capacity Planning, Orchestration, kubernetes, Immutable Infrastructure, Disposable Components, Loosely Coupled, IaaS, Cloud Computing

2022.12.26

ICT技術:ICT Technology

Geometric approach to data

Geometric approaches to data utilized in digital transformation, artificial intelligence, and machine learning tasks (physics, quantum information, online prediction, Bregman divergence, Fisher information matrix, Bethe free energy function, the Gaussian graphical models, semi-positive definite programming problems, positive definite symmetric matrices, probability distributions, dual problems, topological, soft geometry, topology, quantum information geometry, Wasserstein geometry, Lupiner geometry, statistical geometry)

2022.12.25

アルゴリズム:Algorithmsグラフ理論スパースモデリング幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Topological handling of data using topological data analysis

Topological handling of data using topological data analysis utilized for digital transformation, artificial intelligence, and machine learning tasks application to character recognition, application to clustering, R, TDA, barcode plots, persistent plots , python, scikit-tda, Death - Birth, analysis of noisy data, alpha complex, vitris-lips complex, check complex, topological data analysis, protein analysis, sensor data analysis, natural language processing, soft geometry, hard geometry, information geometry, Euclidean Spaces

2022.12.24

アルゴリズム:Algorithmsグラフ理論幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Measures for Stochastic Bandid Problems Stochastic Matching Method and Thompson Extraction

Stochastic bandit problem measures utilized in digital transformation, artificial intelligence, and machine learning tasks Stochastic matching methods and Thompson extraction worst-case riglet minimization, problem-dependent riglet minimization, worst-case riglet upper bounds, problem-dependent riglet, worst-case riglet, and MOSS measures, sample averages, correction terms, UCB liglet upper bounds, adversarial bandit problems, Thompson extraction, Bernoulli distribution, UCB measures, stochastic matching methods, stochastic bandit, Bayesian statistics, KL-UCCB measures, softmax measures, Chernoff-Heffding inequality

2022.12.23

アルゴリズム:Algorithmsオンライン学習バンディッド問題強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Kernel functions as the basis of kernel methods in statistical mathematics theory.

Kernel functions (Gaussian kernels, polynomial kernels, linear kernels, kernel functions, regression functions, linear models, regression problems, discriminant problems) as the basis for kernel methods in statistical mathematics theory used in digital transformation, artificial intelligence and machine learning tasks.

2022.12.22

アルゴリズム:Algorithms微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Basics of gradient method (linear search method, coordinate descent method, steepest descent method and error back propagation method)

Fundamentals of gradient methods utilized in digital transformation, artificial intelligence, and machine learning tasks (linear search, coordinate descent, steepest descent and error back propagation, stochastic optimization, multilayer perceptron, adaboost, boosting, Wolf condition, Zotendijk condition, Armijo condition, backtracking methods, Goldstein condition, strong Wolf condition)

2022.12.20

アルゴリズム:Algorithms微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Machine Learning with Bayesian Inference – Mixture Models, Data Generation Process and Posterior Distribution

Mixture models and data generation processes and posterior distributions (graphical models, Poisson distribution, Gaussian distribution, Dirichlet distribution, categorical distribution) in machine learning with Bayesian inference used in digital transformation, artificial intelligence, machine learning

2022.12.19

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Large-scale Machine Learning with Apache Spark and MLlib

Large-scale machine learning with Apache Spark and MLlib for digital transformation, artificial intelligence, and machine learning tasks (predictive value, RMSE, factor matrix, rank, latent features, neighborhoods, sum of squares error, Mahout, ALS, Scala RDD, alternating least squares, alternating least squares, stochastic gradient descent, persistence, caching, Flambo, Clojure, Java)

2022.12.15

ClojureLarge-Scaleデータアルゴリズム:Algorithms機械学習:Machine Learning