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Reading the Core of Christianity What is the New Testament?

Reading the Core of Christianity What is the New Testament (Gospel of Luke, On the Way to Emmaus, Jesus Walking with Us, Resurrection, Last Supper, Deuterocanonical, Gospel of John, I Am, Parable of the Prodigal Son, Parable of the Good Samaritan)

2022.11.05

キリスト教仏教:Buddhism

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

Protected: Implementation of model-free reinforcement learning in python (1) epsilon-greedy method

Implementation in python of the epsilon-Greedy method, a model-free reinforcement learning method for use in digital transformation, artificial intelligence, and machine learning tasks, multi-armed bandit

2022.11.03

アルゴリズム:Algorithmsマルチエージェントシステム幾何学:Geometry強化学習最適化: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

Hierarchical Temporal Memory and Clojure

Deep learning with hierarchical temporal memory and sparse distributed representation with Clojure for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks

2022.11.02

Clojurepythonアルゴリズム:Algorithmsグラフ理論スパースモデリングスパースモデリング微分積分:Calculus時系列データ解析最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Database construction for microservices using next generation DB Datomic

Building databases with Clojure for microservices using Datomic, the next-generation DB used for digital transformation, artificial intelligence, and machine learning tasks (AWS, five-tuple, immutable, immutable, Datalog, Transactor, Peer Server).

2022.11.01

Clojureアーキテクチャデータベース技術:DataBase Technology

Network analysis using Clojure (1) Width-first/depth-first search, shortest path search, minimum spanning tree, subgraphs and connected components

Network analysis using Clojure/loop for digital transformation , artificial intelligence and machine learning tasks, width-first/depth-first search, shortest path search, minimum spanning tree, subgraph and connected components

2022.10.31

Clojureアルゴリズム:Algorithmsグラフ理論セマンテックウェブ技術:Semantic web Technologyマルチエージェントシステム微分積分: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

Integration of logic and rules with probability/machine learning

Integration of logic and rules with machine learning (inductive logic programming, statistical relational learning, knowledge-based model building, Bayesian nets, probabilistic logic learning, hidden Markov models) used for digital transformation, artificial intelligence, and machine learning tasks.

2022.10.30

Symbolic Logicアルゴリズム:Algorithmsエキスパートシステム:expertsystemグラフ理論セマンテックウェブ技術:Semantic web Technologyベイズ推定微分積分:Calculus推論技術:inference Technology数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics関係データ学習

Protected: Transition from monolithic services to microservices and service design

Overview of migration from monolithic services and design guidelines for microservices leveraged for digital transformation, artificial intelligence, and machine learning tasks (release cycle and deployment process, boundary context, productivity, components, service-oriented architecture, database isolation)

2022.10.29

アーキテクチャコンピューターマルチエージェントシステム

Fundamentals of Continuous Optimization – Calculus and Linear Algebra

Fundamentals of Continuous Optimization - Calculus and Linear Algebra (Taylor's theorem, Hesse matrix, Landau's symbol, Lipschitz continuity, Lipschitz constant, implicit function theorem, Jacobi matrix, diagonal matrix, eigenvalues, nonnegative definite matrix, positive definite matrix, subspace, projection, 1-rank update, natural gradient method, quasi Newton method, Sherman-Morrison formula, norm, Euclidean norm, p-norm, Schwartz inequality, Helder inequality, function on matrix space)

2022.10.28

アルゴリズム:Algorithmsスパースモデリング微分積分: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: Calculation of marginal likelihood, posterior mean, posterior covariance, and predictive distribution using variational Bayesian methods

Methods for computing marginal likelihoods, posterior means, posterior covariances, and predictive distributions in variational Bayesian methods for digital transformation, artificial intelligence , and machine learning tasks James Stein estimator, maximum likelihood estimation, empirical Bayes estimator, Bayesian free energy, hyperparameters, automatic relevance determination, linear regression models, stochastic complexity, log marginal likelihood empirical Bayesian learning, multinomial distribution models, posterior means, linear regression models

2022.10.27

アルゴリズム:Algorithms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