C/C++ Overview of Unity and its integration with external systems
Unity Overview
Unity is an integrated development environment (IDE) for game and application development de...
マルチエージェントシステム
C/C++ python Overview of automatic statement generation using Huggingface
Huggingface
Huggingface is an open source platform and library for machine learning and natural language pro...
アルゴリズム:Algorithms 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
アルゴリズム:Algorithms 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)
アルゴリズム:Algorithms 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)
web技術:web technology 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
web技術:web technology 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)
IOT技術:IOT Technology 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)
アーキテクチャ 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
Clojure 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