アルゴリズム:Algorithms Overview of ReAct (Reasoning and Acting) and examples of its implementation
Overview of ReAct(Reasoning and Acting)
ReAct is one of the prompt engineering methods described in "Overvie...
マルチエージェントシステム
アルゴリズム:Algorithms アルゴリズム:Algorithms Graph Neural Network
Features and Applications of Graph Neural Networks
Overview
Graph data, as described in "Graph Data Proces...
アルゴリズム:Algorithms Agents and Tools in LangChain
Introduction
This section continues the discussion of LangChain, as described in "Overview of ChatGPT and La...
python Overview of Prompt Engineering and its use
Overview of Prompt Engineering
BERT, described in "BERT Overview, Algorithm and Examples of Implementation,"...
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...
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