ML | Page 15 | Deus Ex Machina

Protected: Clojure implementation of distributed computation processing (map-reduce) used in Hadoop

Clojure implementation of distributed computation processing (map-reduce) used in Hadoop for digital transformation, artificial intelligence, and machine learning tasks Tesser, Reducer function, fold, cost function, gradient descent method, feature extraction, feature-scales function, feature scaling, gradient descent learning rate, gradient descent update rule, iterative algorithm, multiple regression, correlation matrix, fuse, commutative, linear regression, co-reduction, and covariance) feature-scales function, feature scaling, gradient descent learning rate, gradient descent update rule, iterative algorithm, multiple regression, correlation matrix, fuse, commutativity, linear regression, covariance, Hadoop, pararrel fold

2022.11.16

ClojureLarge-Scaleデータ機械学習:Machine Learning

Security in Microservices – APIs with Auth and Pedestal in Clojure

Security in microservices leveraged for digital transformation, artificial intelligence, and machine learning tasks - APIs with Auth and Pedestal in Clojure Buddy, Buddy sign, JSON Web Tokens, JSON Web Signature, JSON Web Encryption

2022.11.15

Clojureアーキテクチャセキュリティマルチエージェントシステム

Protected: Apache Spark’s processing model for distributed data processing

Used for digital transformation artificial intelligence and machine learning tasks Apache Spark's processing model (Executor, Task, Scheduler, Driver Program, Master Node, Worker Node, Spark Standalone, Mesos, Hadoop, HFDS, YARN, Partitions, RDD, Transformations, Actions, Resillient Distributed Dataset)

2022.11.14

IOT技術:IOT TechnologypythonRStream Data Processingweb技術:web technologyデータベース技術:DataBase Technologyユーザーインターフェース/データビジュアライゼーション時系列データ解析非同期/並行処理:Asynchronous/parallel processing

Protected: Information Geometry of Positive Definite Matrices (1) Introduction of dual geometric structure

Introduction of dual geometric structures as information geometry for positive definite matrices utilized in digital transformation, artificial intelligence, and machine learning tasks (Riemannian metric, tangent vector space, semi-positive definite programming problem, self-equilibrium, Levi-Civita connection, Riemannian geometry, geodesics, Euclidean geometry, ∇-geodesics, tangent vector, tensor quantity, dual flatness, positive definite matrix set)

2022.11.12

グラフ理論スパースモデリングベイズ推定数理論理学最適化: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: Fundamentals of Stochastic Bandid Problems

Basics of stochastic bandid problems utilized in digital transformation, artificial intelligence, and machine learning tasks (large deviation principle and examples in Bernoulli distribution, Chernoff-Heffding inequality, Sanov's theorem, Heffding inequality, Kullback-Leibler divergence, probability mass function, hem probability, probability approximation by central limit theorem).

2022.11.11

バンディッド問題強化学習微分積分:Calculus機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Fundamentals of Convex Analysis as a Basic Matter for Sequential Optimization in Machine Learning

Basics of convex analysis as a fundamental matter of continuous optimization utilized in digital transformation, artificial intelligence, and machine learning tasks subgradient, subdifferential, conjugate function, closed truly convex function, conjugate function, strongly convex function, closed truly convex function, upper and lower bounds on function values, Hesse matrix, epigraph, Taylor's theorem, relative interior, Ahuynh envelope, continuity, convex envelope, convex function, convex set

2022.11.10

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: Basic Framework of Statistical Mathematics Theory

Basic framework of statistical mathematics theory used in digital transformation, artificial intelligence, and machine learning tasks regularization, approximation and estimation errors, Höfding's inequality, prediction discriminant error, statistical consistency, learning algorithms, performance evaluation, ROC curves, AUC, Bayes rules, Bayes error, prediction loss, empirical loss

2022.11.09

アルゴリズム:Algorithmsグラフ理論微分積分: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: Supervised learning and regularization

Overview of supervised learning regression, discriminant and regularization ridge function, L1 regularization, bridge regularization, elastic net regularization, SCAD, group regularization, generalized concatenated regularization, trace norm regularization as the basis of machine learning optimization methods used for digital transformation, artificial intelligence and machine learning tasks

2022.11.08

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

Protected: Spatial statistics of Gaussian processes, with application to Bayesian optimization

Spatial statistics of Gaussian processes as an application of stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks, and tools ARD, Matern kernelsfor Bayesian optimization GPyOpt and GPFlow and GPyTorch

2022.11.07

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

UML, workflow data visualization tool plantUML

UML for digital transformation , artificial intelligence , and machine learning tasks, workflow data visualization tools plantUML

2022.11.04

ClojureJavapythonユーザーインターフェース/データビジュアライゼーション