February 2023 | Deus Ex Machina

Systems Thinking Approach and the SDGs

Systems thinking approaches and SDGs that are useful in examining the challenges of tasks such as machine learning, artificial intelligence, and digital transformation leverage points, system dynamics modeling, stock & flow, system archetypes, loop diagrams, CLD, time-series change pattern graphs, BOT

2023.02.28

組織活性化:revitalize an organization経済学課題解決:Problem solving

Houjouki” Question the value of abundance.

Summary The Hojoki is a Japanese essay written in the Kamakura period (1185-1333) by the Zen priest Kamo Chōmei. T...

2023.02.27

紀行読書

Overview of Container Technology and Docker for Cloud Native

Overview of container technologies that enable cloud-native utilized for digital transformation, artificial intelligence, and machine learning tasks and Docker container images, build/push functionality, cri-o, container execution, Kubernetes, the containerd, CRI-Containerd, low-level container runtime, high-level container runtime, kernel functionality, Open Container Initiative, OCI, Runtime Specification, Format Specification, Copy-on-Write, COW, cgroups, hierarchies, file systems, namespaces, virtual OS, Paas, Linux

2023.02.27

web技術:web technologyアーキテクチャコンピューターネットワーク技術プログラミング言語:Programming Language

Operating system e.g. Linux

Operating system e.g. Linux An operating system is a type of software that manages the entire system, imple...

2023.02.26

IOT技術:IOT Technologyアーキテクチャコンピューター

Cloud Computing Technology

Cloud Computing Technology Cloud computing will be a service that provides computing resources using multip...

2023.02.26

web技術:web technologyクラウド技術コンピューター

The computing elements and semiconductor chips that make up a computer

Computational elements and semiconductor chips that make up computers utilized for digital transformation, artificial intelligence, and machine learning tasks (switching, MOSFETs, disjunction, conjunction, negation, silicon, statistical physics, dopants, periodic table, binary operations, Boolean algebra, bits, bytes)

2023.02.25

アーキテクチャコンピューター人工知能:Artificial Intelligence

Protected: Optimal arm identification and A/B testing in the bandit problem_1

Optimal arm identification and A/B testing in bandit problems for digital transformation, artificial intelligence, and machine learning tasks Heffding's inequality, optimal arm identification, sample complexity, sample complexity, riglet minimization, cumulative riglet minimization, cumulative reward maximization, ε-optimal arm identification, simple riglet minimization, ε-best arm identification, KL-UCB strategy, KL divergence) cumulative reward maximization, ε-optimal arm identification, simple liglet minimization, ε-best arm identification, KL-UCB strategy, KL divergence, A/B testing of the normal distribution, fixed confidence, fixed confidence

2023.02.24

アルゴリズム:Algorithmsグラフ理論バンディッド問題幾何学: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: Overview of nu-Support Vector Machines by Statistical Mathematics Theory

Overview of nu-support vector machines by statistical mathematics theory utilized in digital transformation, artificial intelligence, and machine learning tasks (kernel functions, boundedness, empirical margin discriminant error, models without bias terms, reproducing nuclear Hilbert spaces, prediction discriminant error, uniform bounds Statistical Consistency, C-Support Vector Machines, Correspondence, Statistical Model Degrees of Freedom, Dual Problem, Gradient Descent, Minimum Distance Problem, Discriminant Bounds, Geometric Interpretation, Binary Discriminant, Experience Margin Discriminant Error, Experience Discriminant Error, Regularization Parameter, Minimax Theorem, Gram Matrix, Lagrangian Function).

2023.02.23

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集合論:Set theory

Protected: Stochastic coordinate descent as a distributed process for batch stochastic optimization

Stochastic coordinate descent as a distributed process for batch stochastic optimization utilized in digital transformation, artificial intelligence, and machine learning tasks (COCOA, convergence rate, SDCA, γf-smooth, approximate solution of subproblems, stochastic coordinate descent, parallel stochastic coordinate descent, parallel computing process, Communication-Efficient Coordinate Ascent, dual coordinate descent)

2023.02.22

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

Protected: Quasi-Newton Method as Sequential Optimization in Machine Learning(1) Algorithm Overview

Quasi-Newton methods as continuous machine learning optimization for digital transformation, artificial intelligence, and machine learning tasks (BFGS formulas, Lagrange multipliers, optimality conditions, convex optimization problems, KL divergence minimization, equality constrained optimization problems, DFG formulas, positive definite matrices, geometric structures, secant conditions, update laws for quasi-Newton methods, Hesse matrices, optimization algorithms, search directions, Newton methods)

2023.02.21

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

2023-02