web技術:web technology

Protected: Microservices Architecture

The ideal enterprise system would be tightly integrated and provide all business functions as a single unit optimized for a particular technology stack and hardware. Such monolithic systems often become more complex over time and difficult to understand as a single unit by a single team. Domain-driven design advocates decomposing such systems into smaller modular components and assigning them to teams that focus on a single business capability in a limited context.
アルゴリズム:Algorithms

Protected: Meta-analysis in Medical Research Methods of Evidence Integration in Scientific Evidence-Based Medicine

Evidence integration in meta-analysis in science-based medicine as statistical data processing in digital transformation, artificial intelligence, and machine learning tasks method of moments, maximum likelihood, large sample theory, DerSimonian an Laird estimation, publication bias, network meta-analysis
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Thinking Machines Machine Learning and its Hardware Implementation

Summary Many hardware implementations of machine learning are dedicated hardware. These means include the f...
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KI 2017: Advances in Artificial Intelligence Papers

KI2017 In the previous article we discussed KI2016. In this issue, we describe KI2017, which w...
アルゴリズム:Algorithms

Protected: Support Vector Machines for Weak Label Learning (2) Multi-Instance Learning

Extension of support vector machines utilized for digital transformation, artificial intelligence, and machine learning tasks; multi-instance learning approach with SVMs for weak-label learning problems (mi-SVM, MI-SVM)
アルゴリズム:Algorithms

Protected: Computation of graphical models with hidden variables

Parameter learning of graphical models with hidden variables using variational EM algorithm in stochastic generative models (wake-sleep algorithm, MCEM algorithm, stochastic EM algorithm, Gibbs sampling, contrastive divergence method, constrained Boltzmann machine, EM algorithm, KL divergence)
アルゴリズム:Algorithms

Protected: Application of Variational Bayesian Algorithm to Matrix Decomposition Models with Missing Values

Application of variational Bayesian algorithm to matrix factorization models with missing values as a stochastic generative model computation for use in digital transformation, artificial intelligence, and machine learning tasks
アルゴリズム:Algorithms

Protected: Application of Nonparametric Bayesian Structural Change Estimation

Nonparametric Bayesian structural change estimation using Gibbs sampling as an application of probabilistic generative models for digital transformation, artificial intelligence, and machine learning tasks
アルゴリズム:Algorithms

Protected: Stochastic Generative Models and Gaussian Processes(2)Maximum Likelihood and Bayesian Estimation

Maximum Likelihood and Bayesian Estimation Overview for Probabilistic Generative Models and Gaussian Process Fundamentals Used in Digital Transformation, Artificial Intelligence, and Machine Learning Tasks

On the Road – Kobe Walk

Kobe Stroll from Ryotaro Shiba's Kaido Yuku (Old Foreign Settlement, Yamanote, Kobe Oriental Hotel, Hong Kong Shanghai Bank, Ikuta River, Katsu Kaishu, Cape Wada, Battery, Water of Rokko, Nunobiki Waterfall, Shin-Kobe Station, Sannomiya, Port Island, Kobe Portopia Hotel, Nagata Ward, Giant Robot, Million Dollar Night View)
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