Deus Ex Machina

On the Road to Shirakawa and Aizu

Ryotaro Shiba's Road to Shirakawa and Aizu Masayuki Hoshina, Aizu Culture, Ujisato Gamo, Aizu Wakamatsu, Boshin War, Ou-Koshi Alliance, Yoshinobu Tokugawa, Rin Yamashita, Russian Orthodox Church, Iconostas, Holy Image Painting, Shirakawa Christian Orthodox Church Cathedral, Nikolai Cathedral, Resurrection Cathedral, Eihei Hanshokusui, Soto sect, Dogen, Sekikawa Temple, Kim. Yachimizo, Sakai no Myojin, Tamatsushima Myojin, Sumiyoshi Myojin, Matsudaira Sadanobu, Shirakawa Seki, Old Sekiseki, New Shirakawa Station, Tohoku Shinkansen, Genyu, Matsuo Basho, Oku no Hosomichi, Kokin Wakashu, Natori River, trifoliate garden, Nobuo Mojizuri

2022.12.18

旅歴史紀行読書

Protected: Explainable Artificial Intelligence (9) Model-independent interpretation (ALE plot)

ALE plot is one of the posterior interpretation models that can be explained and used for digital transformation (DX), artificial intelligence (AI), and machine learning (ML).

2022.12.16

推論技術:inference Technology検索技術:Search Technology機械学習:Machine Learning

Protected: Large-scale Machine Learning with Apache Spark and MLlib

Large-scale machine learning with Apache Spark and MLlib for digital transformation, artificial intelligence, and machine learning tasks (predictive value, RMSE, factor matrix, rank, latent features, neighborhoods, sum of squares error, Mahout, ALS, Scala RDD, alternating least squares, alternating least squares, stochastic gradient descent, persistence, caching, Flambo, Clojure, Java)

2022.12.15

ClojureLarge-Scaleデータアルゴリズム:Algorithms機械学習:Machine Learning

Protected: the application of neural networks to reinforcement learning(1) overview

Overview of the application of neural networks to reinforcement learning utilized in digital transformation, artificial intelligence and machine learning tasks (Agent, Epsilon-Greedy method, Trainer, Observer, Logger, Stochastic Gradient Descent, Stochastic Gradient Descent, SGD, Adaptive Moment Estimation, Adam, Optimizer, Error Back Propagation Method, Backpropagation, Gradient, Activation Function Stochastic Gradient Descent, SGD, Adaptive Moment Estimation, Adam, Optimizer, Error Back Propagation, Backpropagation, Gradient, Activation Function, Batch Method, Value Function, Strategy)

2022.12.14

python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深層学習:Deep Learning確率・統計:Probability and Statistics

Introduction and configuration of Apache Spark for distributed data processing

Deployment and configuration of Apache Spark to enable distributed data processing for digital transformation, artificial intelligence and machine learning tasks (NodeManager, YARN, spark-master, ResourceManager, spark-worker, HDFS, NameNode, DataNode, spark-client, CDH5.4, haddop, Yum, CentOS) spark-worker, HDFS, NameNode, DataNode, spark-client, CDH5.4, haddop, Yum, CentOS)

2022.12.13

IOT技術:IOT TechnologyStream Data Processingweb技術:web technologyアーキテクチャコンピューター時系列データ解析

Deploying and Operating Microservices – Docker and Kubernetes

Deployment and operation of microservices leveraged for digital transformation, artificial intelligence and machine learning tasks - Docker and Kubernetes minikube, containers, deployment, kube-ctl, rolling-upgrade, auto-bin packing, horizontal scaling, scale-up, scale-down, self-healing, kubelet, kube-apiserver, etcd, kube-controller- manager, kube- scheduler, pod, kube-proxy, Docker CLI, the Docker Registry, cgroups, Linux kernel, kernel namespace, union mount option, Hypervisor

2022.12.12

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

Machine Learning by Ensemble Methods – Fundamentals and Algorithms Reading Notes

Fundamentals and algorithms in machine learning with ensemble methods used in digital transformation, artificial intelligence and machine learning tasks class unbalanced learning, cost-aware learning, active learning, semi-supervised learning, similarity-based methods, clustering ensemble methods, graph-based methods, festival label-based methods, transformation-based methods, clustering, optimization-based pruning, ensemble pruning, join methods, bagging, boosting

2022.12.11

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: Information Geometry of Positive Definite Matrices (3)Calculation Procedure and Curvature

Procedures and curvature of computation of positive definite matrices as informative geometry utilized in digital transformation, artificial intelligence, and machine learning tasks

2022.12.10

アルゴリズム:Algorithms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: Measures for Stochastic Banded Problems Likelihood-based measures (UCB and MED measures)

Measures for Stochastic Banded Problems Likelihood-based UCB and MED measures (Indexed Maximum Empirical Divergence policy, KL-UCB measures, DMED measures, Riglet upper bound, Bernoulli distribution, Large Deviation Principle, Deterministic Minimum Empirical Divergence policy, Newton's method, KL divergence, Binsker's inequality, Heffding's inequality, Chernoff-Heffding inequality, Upper Confidence Bound)

2022.12.09

アルゴリズム:Algorithms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

Protected: Overview of Discriminant Adaptive Losses in Statistical Mathematics Theory

Overview of Discriminant Conformal Losses in Statistical Mathematics Theory (Ramp Losses, Convex Margin Losses, Nonconvex Φ-Margin Losses, Discriminant Conformal, Robust Support Vector Machines, Discriminant Conformity Theorems, L2-Support Vector Machines, Squared Hinge Loss, Logistic Loss, Hinge Loss, Boosting, Exponential Losses, Discriminant Conformity Theorems for Convex Margin Losses, Bayes Rules, Prediction Φ-loss, Prediction Discriminant Error, Monotonic Nonincreasing Convex Function, Empirical Φ-loss, Empirical Discriminant Error)

2022.12.08

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