digital transformation

Protected: Network Analysis Using Clojure (2)Computing Triangles in a Graph Using Glittering

Network analysis using triangle computation in graphs using Clojure/Glittering for digital transformation, artificial intelligence, and machine learning tasks (GraphX, Pregel API, Twitter dataset, custom triangle count algorithm, message send function, message merge function, outer join, RDD, vertex attributes, Apache Spark, Sparkling, MLlib, Glittering, triangle counting, edge-cut strategy, random-vertex-cut strategy, and social networks, graph parallel computing functions, Hadoop, data parallel systems, RDG, Resilient Distributed Graph, Hama, Giraph)

2023.01.18

Clojureアルゴリズム:Algorithmsグラフ理論最適化:Optimization機械学習:Machine Learning線形代数:Linear Algebra集合論:Set theory

Protected: Setup of Terraform, an infrastructure management tool

Setup of Terraform, an infrastructure management tool used for digital transformation, artificial intelligence, and machine learning tasks (git-secrets, dockernized Terraform, AWS credentials, team development tfenv, Homebrew, AWS CLI, AWS Management Console, access key ID, secret access key, python, Identity and Access Management, AWS, environment setup)

2023.01.17

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マルチエージェントシステム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

AWS Cloud Service Design Patterns (1)

AWS Cloud Service Design Patterns for Digital Transformation, Artificial Intelligence, and Machine Learning tasks (Snapshot, Stamp, Scale Up, Scale Out, Ondemand Disk, Multi-Server, Multi-Datacenter, Floating IP, Deep Health Check, Routing-Based HA, Clone Server, NFS Sharing, NFS Replica, State Sharing, URL Rewriting, Rewrite Proxy, and Datacenter). Datacenter, Floating IP, Deep Health Check, Routing-Based HA, Clone Server, NFS Sharing, NFS Replica, State Sharing, URL Rewriting, Rewrite Proxy, Cache Proxy, Scheduled Scale Cache Proxy, Scheduled Scale Out, IP Pooling, Web Storage, Direct Hosting, Private Distribution, Cache Distribution, Rename Distribution, Private Cache Distribution, Latency Based Origin)

2023.01.16

アーキテクチャコンピューター

Protected: Regret Analysis for Stochastic Banded Problems

Regret analysis for stochastic banded problems utilized in digital transformation, artificial intelligence, and machine learning tasks (sum of equal sequences, gamma function, Thompson extraction, beta distribution, hem probability, Mills ratio, partial integration, posterior sample, conjugate prior distribution, Bernoulli distribution, cumulative distribution function, expected value, DMED measure, UCB measure, Chernoff-Hefding inequality, likelihood, upper bound, lower bound, UCB score, arms)

2023.01.13

アルゴリズム:Algorithms強化学習微分積分: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: Regenerate nuclear Hilbert spaces as a basis for kernel methods in statistical mathematics theory.

Regenerate kernel Hilbert spaces as a basis for kernel methods in statistical mathematics theory used in digital transformation, artificial intelligence, and machine learning tasks orthonormal basis, Hilbert spaces, Gaussian kernels, continuous functions, kernel functions, complete spaces, inner product spaces, equivalence classes, equivalence relations, Cauchy sequences, linear spaces, norms, complete inner products

2023.01.12

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

Protected: Batch Stochastic Optimization – Stochastic Dual Coordinate Descent

Stochastic dual coordinate descent algorithms as batch-type stochastic optimization utilized in digital transformation, artificial intelligence, and machine learning tasks Nesterov's measurable method, SDCA, mini-batch, computation time, batch proximity gradient method, optimal solution, operator norm, maximum eigenvalue , Fenchel's dual theorem, principal problem, dual problem, proximity mapping, smoothing hinge loss, on-line type stochastic optimization, elastic net regularization, ridge regularization, logistic loss, block coordinate descent method, batch type stochastic optimization

2023.01.11

アルゴリズム:Algorithms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: Newtonian and Modified Newtonian Methods as Sequential Optimization in Machine Learning

Newton and modified Newton methods (Cholesky decomposition, positive definite matrix, Hesse matrix, Newtonian direction, search direction, Taylor expansion) as continuous machine learning optimization for digital transformation, artificial intelligence and machine learning tasks

2023.01.10

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

Protected: What triggers sparsity and for what kinds of problems is sparsity appropriate?

What triggers sparsity and for what kinds of problems is sparsity suitable for sparse learning as it is utilized in digital transformation, artificial intelligence, and machine learning tasks? About alternating direction multiplier method, sparse regularization, main problem, dual problem, dual extended Lagrangian method, DAL method, SPAMS, sparse modeling software, bioinformatics, image denoising, atomic norm, L1 norm, trace norm, number of nonzero elements

2023.01.09

アルゴリズム:Algorithmsスパースモデリング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: Big Data and Bayesian Learning – The Importance of Small Data Learning

Big Data and Bayesian Learning for Digital Transformation (DX), Artificial Intelligence (AI), and Machine Learning (ML) Tasks - Importance of Small Data Learning

2023.01.07

アルゴリズム:Algorithmsグラフ理論ベイズ推定幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Theory of Noisy L1-Norm Minimization as Machine Learning Based on Sparsity (1)

Theory of L1 norm minimization with noise as sparsity-based machine learning for digital transformation, artificial intelligence, and machine learning tasks Markov's inequality, Heffding's inequality, Berstein's inequality, chi-square distribution, hem probability, union Bound, Boolean inequality, L∞ norm, multidimensional Gaussian spectrum, norm compatibility, normal distribution, sparse vector, dual norm, Cauchy-Schwartz inequality, Helder inequality, regression coefficient vector, threshold, k-sparse, regularization parameter, inferior Gaussian noise

2023.01.06

スパースモデリング機械学習:Machine Learning