紀行 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...
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紀行 web技術:web technology 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
IOT技術:IOT Technology Operating system e.g. Linux
Operating system e.g. Linux
Overview
An operating system is a type of software that manages the entire sys...
web技術:web technology Cloud Computing Technology
Cloud Computing Technology
Overview
Cloud computing will be a service that provides computing resources us...
アーキテクチャ 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)
アルゴリズム:Algorithms 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
アルゴリズム:Algorithms 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).
アルゴリズム:Algorithms 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)
アルゴリズム:Algorithms 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)
アルゴリズム:Algorithms Protected: Example of Machine Learning with Bayesian Inference: Variational Inference for Poisson Mixture Models
Examples of machine learning with Bayesian inference utilized for digital transformation, artificial intelligence, and machine learning tasks: variational inference for Poisson mixed models (Gibbs sampling, variational inference, algorithm, ELBO, computation, variational inference algorithm, latent variable parameters, posterior distribution, Dirichlet distribution, gamma distribution)