マルチエージェントシステム

Protected: Optimal arm bandit and Bayesian optimal when the player’s candidate actions are huge or continuous (2)

Bayesian optimization for digital transformation, artificial intelligence, machine learning tasks and bandit when player behavior is massive/continuous Markov chain Monte Carlo, Monte Carlo integration, turn kernels, scale parameters, Gaussian kernels, covariance function parameter estimation, Simultaneous Optimistic Optimazation policy, SOO strategy, algorithms, GP-UCB policy, Thompson's law, expected value improvement strategy, GP-UCB policy

2023.05.05

アルゴリズム:Algorithmsグラフ理論スパースモデリングバンディッド問題マルチエージェントシステム幾何学:Geometry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

Protected: Implementation of two approaches to improve environmental awareness, a weak point of deep reinforcement learning.

Implementation of two approaches to improve environment awareness, a weakness of deep reinforcement learning used in digital transformation, artificial intelligence, and machine learning tasks (inverse predictive, constrained, representation learning, imitation learning, reconstruction, predictive, WorldModels, transition function, reward function Weaknesses of representation learning, VAE, Vision Model, RNN, Memory RNN, Monte Carlo methods, TD Search, Monte Carlo Tree Search, Model-based learning, Dyna, Deep Reinforcement Learning)

2023.04.27

アルゴリズム:Algorithmsグラフ理論スパースモデリングマルチエージェントシステム幾何学:Geometry強化学習微分積分:Calculus数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics

Protected: Application of Neural Networks to Reinforcement Learning Policy Gradient, which implements a strategy with a function with parameters.

Application of Neural Networks to Reinforcement Learning for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Policy Gradient to implement strategies with parameterized functions (discounted present value, strategy update, tensorflow, and Keras, CartPole, ACER, Actor Critoc with Experience Replay, Off-Policy Actor Critic, behavior policy, Deterministic Policy Gradient, DPG, DDPG, and Experience Replay, Bellman Equation, policy gradient method, action history)

2023.02.16

アルゴリズム:Algorithmsグラフ理論スパースモデリングマルチエージェントシステム幾何学:Geometry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: On cloud-native and service-centric development

On cloud-native and service-centric development leveraged for digital transformation, artificial intelligence, and machine learning tasks inter-organizational, siloed, KPIs, business value, Conway's Law, organizational restructuring, process reform, CNCF Incubating Stage, CNCF Graduate Stage, CNCF Sandbox Stage, Technical Oversight Committee, End User Advisory Board, Cloud Native Application Development, Kubernetes Application Modernization, The Twelve-Factor App, 12 Application Principles, Container Orchestration, APIs, Service Based Architecture, SOA, Service Oriented Architecture, Microservices, Sparse Coupling, Delivery Performance, MTTR, Lead Time, Change Loss Rate, Deployment Frequency, Docker

2023.02.13

web技術:web technologyアーキテクチャコンピューター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

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

Protected: Leveraging Apache Spark for Distributed Data Processing – Developing and Executing Applications

Leveraging Apache Spark to enable distributed data processing for digital transformation, artificial intelligence, and machine learning tasks -Application development and execution (forced termination, yarn-client mode, yarn-cluster mode, YARN, and YARN) management commands, cluster, python, Clojure, Shell, AWS, Glue, sparkplug, spark-shell, spark-submit, Nodemanager, HDFS, Spark applications, Scala, sbt, plugin.sbt, build.sbt build.sbt, build, sbt-assembly plugin, JAR file)

2022.12.27

IOT技術:IOT TechnologyStream Data Processingweb技術:web technologyアーキテクチャデータベース技術:DataBase Technologyネットワーク技術ハードウェアマルチエージェントシステム時系列データ解析機械学習:Machine Learning

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

Use of ElasticStash for monitoring system operations, including microservices

Leveraging ElasticStash for system operations monitoring, including microservices leveraged for digital transformation artificial intelligence, and machine learning tasks Riemann, rollup, throttle structure, KafKa plugin, UTC, timbre LogStash, log4j, tools.logging, structured logging, common log formats, visualization features, dashboards, Kibana, pipeline, UDP, Collectd, RRD, stdin, stdout, ELK Stack, Elastic Stack Apache Kafka

2022.11.28

Clojureアーキテクチャコンピューターセキュリティデータベース技術:DataBase Technologyマルチエージェントシステム

Protected: Implementation of model-free reinforcement learning in python (2) Monte Carlo and TD methods

Python implementations of model-free reinforcement learning such as Monte Carlo and TD methods Q-Learning, Value-based methods, Monte Carlo methods, neural nets, Epsilon-Greedy methods, TD(lambda) methods, Muli-step Learning, Rainbow, A3C/A2C, DDPG, APE-X DDPG, Muli-step Learning) Epsilon-Greedy method, TD(λ) method, Muli-step Learning, Rainbow, A3C/A2C, DDPG, APE-X DQN

2022.11.17

アルゴリズム:Algorithmsマルチエージェントシステム強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Security in Microservices – APIs with Auth and Pedestal in Clojure

Security in microservices leveraged for digital transformation, artificial intelligence, and machine learning tasks - APIs with Auth and Pedestal in Clojure Buddy, Buddy sign, JSON Web Tokens, JSON Web Signature, JSON Web Encryption

2022.11.15

Clojureアーキテクチャセキュリティマルチエージェントシステム