Agile_Intelligence(1)
人工智能中ai,aigc,agi,ai agent名词解释
人工智能中ai,aigc,agi,ai agent名词解释AI：人工智能（Artificial Intelligence）的英文缩写，是一门研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术科学。
AI是智能学科重要的组成部分，它企图了解智能的实质，并生产出一种新的能以人类智能相似的方式做出反应的智能机器，该领域的研究包括机器人、语言识别、图像识别、自然语言处理和专家系统等。
AI可以对人的意识、思维的信息过程的模拟。
AI不是人的智能，但能像人那样思考、也可能超过人的智能。
AI是一门极富挑战性的科学，从事这项工作的人必须懂得计算机知识，心理学和哲学等。
AI是包括十分广泛的科学，它由不同的领域组成，如机器学习，计算机视觉等等。
AIGC：全名是指“Artificial Intelligence Generated Content”，即利用人工智能技术来生成内容的一种新型技术。
通俗讲是用人工智能进行产品的内容创作。
比如，让AI根据一句话创作出一幅画；让AI根据几个词写代码；编写规则输入AI，使AI能够进行实时人机互动等等。
AGI：Artificial General Intelligence的首字母缩写，意为人工通用智能。
它是一种可以执行复杂任务的人工智能，能够完全模仿人类智能的行为，能够执行任何人类智能活动的计算机系统。
AGI可以被认为是人工智能的更高层次，它可以实现自我学习、自我改进、自我调整，进而解决任何问题而不需要人为干预。
AI Agent：在人工智能领域，“agent”是指一个可以感知环境并采取行动的实体。
Agent可以是物理实体（例如机器人），也可以是虚拟实体（例如计算机程序）。
Agent通常具备某种程度的自主性和智能，能够根据环境中的信息和设定的目标来做出决策和执行动作。
Agent的设计目标是为了解决特定的问题或完成特定的任务。
它可以接收来自环境的输入信息，并通过一些算法、推理或学习技术来处理这些信息，然后基于这些处理结果做出相应的动作或决策。
100个信息工程专业术语中英文
100个信息工程专业术语中英文全文共3篇示例，供读者参考篇1Information engineering is a vast field that covers a wide range of knowledge and skills. In this article, we will introduce 100 important terms and concepts in information engineering, both in English and Chinese.1. Artificial Intelligence (AI) - 人工智能2. Machine Learning - 机器学习3. Deep Learning - 深度学习4. Natural Language Processing (NLP) - 自然语言处理5. Computer Vision - 计算机视觉6. Data Mining - 数据挖掘7. Big Data - 大数据8. Internet of Things (IoT) - 物联网9. Cloud Computing - 云计算10. Virtual Reality (VR) - 虚拟现实11. Augmented Reality (AR) - 增强现实12. Cybersecurity - 网络安全13. Cryptography - 密码学14. Blockchain - 区块链15. Information System - 信息系统16. Database Management System (DBMS) - 数据库管理系统17. Relational Database - 关系数据库18. NoSQL - 非关系型数据库19. SQL (Structured Query Language) - 结构化查询语言20. Data Warehouse - 数据仓库21. Data Mart - 数据集市22. Data Lake - 数据湖23. Data Modeling - 数据建模24. Data Cleansing - 数据清洗25. Data Visualization - 数据可视化26. Hadoop - 分布式存储和计算框架27. Spark - 大数据处理框架28. Kafka - 流数据处理平台29. Elasticsearch - 开源搜索引擎30. Cyber-Physical System (CPS) - 嵌入式系统31. System Integration - 系统集成32. Network Architecture - 网络架构33. Network Protocol - 网络协议34. TCP/IP - 传输控制协议/互联网协议35. OSI Model - 开放系统互连参考模型36. Router - 路由器37. Switch - 交换机38. Firewall - 防火墙39. Load Balancer - 负载均衡器40. VPN (Virtual Private Network) - 虚拟专用网络41. SDN (Software-Defined Networking) - 软件定义网络42. CDN (Content Delivery Network) - 内容分发网络43. VoIP (Voice over Internet Protocol) - 互联网语音44. Unified Communications - 统一通信45. Mobile Computing - 移动计算46. Mobile Application Development - 移动应用开发47. Responsive Web Design - 响应式网页设计48. UX/UI Design - 用户体验/用户界面设计49. Agile Development - 敏捷开发50. DevOps - 开发与运维51. Continuous Integration/Continuous Deployment (CI/CD) - 持续集成/持续部署52. Software Testing - 软件测试53. Bug Tracking - 缺陷跟踪54. Version Control - 版本控制55. Git - 分布式版本控制系统56. Agile Project Management - 敏捷项目管理57. Scrum - 敏捷开发框架58. Kanban - 看板管理法59. Waterfall Model - 瀑布模型60. Software Development Life Cycle (SDLC) - 软件开发生命周期61. Requirements Engineering - 需求工程62. Software Architecture - 软件架构63. Software Design Patterns - 软件设计模式64. Object-Oriented Programming (OOP) - 面向对象编程65. Functional Programming - 函数式编程66. Procedural Programming - 过程式编程67. Dynamic Programming - 动态规划68. Static Analysis - 静态分析69. Code Refactoring - 代码重构70. Code Review - 代码审查71. Code Optimization - 代码优化72. Software Development Tools - 软件开发工具73. Integrated Development Environment (IDE) - 集成开发环境74. Version Control System - 版本控制系统75. Bug Tracking System - 缺陷跟踪系统76. Code Repository - 代码仓库77. Build Automation - 构建自动化78. Continuous Integration/Continuous Deployment (CI/CD) - 持续集成/持续部署79. Code Coverage - 代码覆盖率80. Code Review - 代码审查81. Software Development Methodologies - 软件开发方法论82. Waterfall Model - 瀑布模型83. Agile Development - 敏捷开发84. Scrum - 看板管理法85. Kanban - 看板管理法86. Lean Development - 精益开发87. Extreme Programming (XP) - 极限编程88. Test-Driven Development (TDD) - 测试驱动开发89. Behavior-Driven Development (BDD) - 行为驱动开发90. Model-Driven Development (MDD) - 模型驱动开发91. Design Patterns - 设计模式92. Creational Patterns - 创建型模式93. Structural Patterns - 结构型模式94. Behavioral Patterns - 行为型模式95. Software Development Lifecycle (SDLC) - 软件开发生命周期96. Requirement Analysis - 需求分析97. System Design - 系统设计98. Implementation - 实施99. Testing - 测试100. Deployment - 部署These terms are just the tip of the iceberg when it comes to information engineering. As technology continues to advance, new terms and concepts will emerge, shaping the future of this dynamic field. Whether you are a student, a professional, or just someone interested in technology, familiarizing yourself with these terms will help you navigate the complex world of information engineering.篇2100 Information Engineering Professional Terms in English1. Algorithm - a set of instructions for solving a problem or performing a task2. Computer Science - the study of computers and their applications3. Data Structures - the way data is organized in a computer system4. Networking - the practice of linking computers together to share resources5. Cybersecurity - measures taken to protect computer systems from unauthorized access or damage6. Software Engineering - the application of engineering principles to software development7. Artificial Intelligence - the simulation of human intelligence by machines8. Machine Learning - a type of artificial intelligence that enables machines to learn from data9. Big Data - large and complex sets of data that require specialized tools to process10. Internet of Things (IoT) - the network of physical devices connected through the internet11. Cloud Computing - the delivery of computing services over the internet12. Virtual Reality - a computer-generated simulation of a real or imagined environment13. Augmented Reality - the integration of digital information with the user's environment14. Data Mining - the process of discovering patterns in large data sets15. Quantum Computing - the use of quantum-mechanical phenomena to perform computation16. Cryptography - the practice of securing communication by encoding it17. Data Analytics - the process of analyzing data to extract meaningful insights18. Information Retrieval - the process of finding relevant information in a large dataset19. Web Development - the process of creating websites and web applications20. Mobile Development - the process of creating mobile applications21. User Experience (UX) - the overall experience of a user interacting with a product22. User Interface (UI) - the visual and interactive aspects of a product that a user interacts with23. Software Architecture - the design and organization of software components24. Systems Analysis - the process of studying a system's requirements to improve its efficiency25. Computer Graphics - the creation of visual content using computer software26. Embedded Systems - systems designed to perform a specific function within a larger system27. Information Security - measures taken to protect information from unauthorized access28. Database Management - the process of organizing and storing data in a database29. Cloud Security - measures taken to protect data stored in cloud computing environments30. Agile Development - a software development methodology that emphasizes collaboration and adaptability31. DevOps - a set of practices that combine software development and IT operations to improve efficiency32. Continuous Integration - the practice of integrating code changes into a shared repository frequently33. Machine Vision - the use of cameras and computers to process visual information34. Predictive Analytics - the use of data and statistical algorithms to predict future outcomes35. Information Systems - the study of how information is used in organizations36. Data Visualization - the representation of data in visual formats to make it easier to understand37. Edge Computing - the practice of processing data closer to its source rather than in a centralized data center38. Natural Language Processing - the ability of computers to understand and generate human language39. Cyber Physical Systems - systems that integrate physical and computational elements40. Computer Vision - the ability of computers to interpret and understand visual information41. Information Architecture - the structural design of information systems42. Information Technology - the use of computer systems to manage and process information43. Computational Thinking - a problem-solving approach that uses computer science concepts44. Embedded Software - software that controls hardware devices in an embedded system45. Data Engineering - the process of collecting, processing, and analyzing data46. Software Development Life Cycle - the process of developing software from conception to deployment47. Internet Security - measures taken to protectinternet-connected systems from cyber threats48. Application Development - the process of creating software applications for specific platforms49. Network Security - measures taken to protect computer networks from unauthorized access50. Artificial Neural Networks - computational models inspired by the biological brain's neural networks51. Systems Engineering - the discipline that focuses on designing and managing complex systems52. Information Management - the process of collecting, storing, and managing information within an organization53. Sensor Networks - networks of sensors that collect and transmit data for monitoring and control purposes54. Data Leakage - the unauthorized transmission of data to an external source55. Software Testing - the process of evaluating software to ensure it meets requirements and functions correctly56. Internet Protocol (IP) - a set of rules for sending data over a network57. Machine Translation - the automated translation of text from one language to another58. Cryptocurrency - a digital or virtual form of currency that uses cryptography for security59. Software Deployment - the process of making software available for use by end-users60. Computer Forensics - the process of analyzing digital evidence for legal or investigative purposes61. Virtual Private Network (VPN) - a secure connection that allows users to access a private network over a public network62. Internet Service Provider (ISP) - a company that provides access to the internet63. Data Center - a facility that houses computing and networking equipment for processing and storing data64. Network Protocol - a set of rules for communication between devices on a network65. Project Management - the practice of planning, organizing, and overseeing a project to achieve its goals66. Data Privacy - measures taken to protect personal data from unauthorized access or disclosure67. Software License - a legal agreement that governs the use of software68. Information Ethics - the study of ethical issues related to the use of information technology69. Search Engine Optimization (SEO) - the process of optimizing websites to rank higher in search engine results70. Internet of Everything (IoE) - the concept of connecting all physical and digital objects to the internet71. Software as a Service (SaaS) - a software delivery model in which applications are hosted by a provider and accessed over the internet72. Data Warehousing - the process of collecting and storing data from various sources for analysis and reporting73. Cloud Storage - the practice of storing data online in remote servers74. Mobile Security - measures taken to protect mobile devices from security threats75. Web Hosting - the service of providing storage space and access for websites on the internet76. Malware - software designed to harm a computer system or its users77. Information Governance - the process of managing information to meet legal, regulatory, and business requirements78. Enterprise Architecture - the practice of aligning an organization's IT infrastructure with its business goals79. Data Backup - the process of making copies of data to protect against loss or corruption80. Data Encryption - the process of converting data into a code to prevent unauthorized access81. Social Engineering - the manipulation of individuals to disclose confidential information82. Internet of Medical Things (IoMT) - the network of medical devices connected through the internet83. Content Management System (CMS) - software used to create and manage digital content84. Blockchain - a decentralized digital ledger used to record transactions85. Open Source - software that is publicly accessible for modification and distribution86. Network Monitoring - the process of monitoring and managing network performance and security87. Data Governance - the process of managing data to ensure its quality, availability, and security88. Software Patch - a piece of code used to fix a software vulnerability or add new features89. Zero-Day Exploit - a security vulnerability that is exploited before the vendor has a chance to patch it90. Data Migration - the process of moving data from one system to another91. Business Intelligence - the use of data analysis tools to gain insights into business operations92. Secure Socket Layer (SSL) - a protocol that encrypts data transmitted over the internet93. Mobile Device Management (MDM) - the practice of managing and securing mobile devices in an organization94. Dark Web - the part of the internet that is not indexed by search engines and often used for illegal activities95. Knowledge Management - the process of capturing, organizing, and sharing knowledge within an organization96. Data Cleansing - the process of detecting and correcting errors in a dataset97. Software Documentation - written information that describes how software works98. Open Data - data that is freely available for anyone to use and redistribute99. Predictive Maintenance - the use of data analytics to predict when equipment will need maintenance100. Software Licensing - the legal terms and conditions that govern the use and distribution of softwareThis list of 100 Information Engineering Professional Terms in English provides a comprehensive overview of key concepts and technologies in the field of information technology. These terms cover a wide range of topics, including computer science, data analysis, network security, and software development. By familiarizing yourself with these terms, you can better understand and communicate about the complex and rapidly evolving world of information engineering.篇3100 Information Engineering Professional Terms1. Algorithm - 算法2. Artificial Intelligence - 人工智能3. Big Data - 大数据4. Cloud Computing - 云计算5. Cryptography - 密码学6. Data Mining - 数据挖掘7. Database - 数据库8. Deep Learning - 深度学习9. Digital Signal Processing - 数字信号处理10. Internet of Things - 物联网11. Machine Learning - 机器学习12. Network Security - 网络安全13. Object-Oriented Programming - 面向对象编程14. Operating System - 操作系统15. Programming Language - 编程语言16. Software Engineering - 软件工程17. Web Development - 网页开发18. Agile Development - 敏捷开发19. Cybersecurity - 网络安全20. Data Analytics - 数据分析21. Network Protocol - 网络协议22. Artificial Neural Network - 人工神经网络23. Cloud Security - 云安全24. Data Visualization - 数据可视化25. Distributed Computing - 分布式计算26. Information Retrieval - 信息检索27. IoT Security - 物联网安全28. Machine Translation - 机器翻译29. Mobile App Development - 移动应用开发30. Software Architecture - 软件架构31. Data Warehousing - 数据仓库32. Network Architecture - 网络架构33. Robotics - 机器人技术34. Virtual Reality - 虚拟现实35. Web Application - 网页应用36. Biometrics - 生物识别技术37. Computer Graphics - 计算机图形学38. Cyber Attack - 网络攻击39. Data Compression - 数据压缩40. Network Management - 网络管理41. Operating System Security - 操作系统安全42. Real-Time Systems - 实时系统43. Social Media Analytics - 社交媒体分析44. Blockchain Technology - 区块链技术45. Computer Vision - 计算机视觉46. Data Integration - 数据集成47. Game Development - 游戏开发48. IoT Devices - 物联网设备49. Multimedia Systems - 多媒体系统50. Software Quality Assurance - 软件质量保证51. Data Science - 数据科学52. Information Security - 信息安全53. Machine Vision - 机器视觉54. Natural Language Processing - 自然语言处理55. Software Testing - 软件测试56. Chatbot - 聊天机器人57. Computer Networks - 计算机网络58. Cyber Defense - 网络防御60. Image Processing - 图像处理61. IoT Sensors - 物联网传感器62. Neural Network - 神经网络63. Network Traffic Analysis - 网络流量分析64. Software Development Life Cycle - 软件开发周期65. Data Governance - 数据治理66. Information Technology - 信息技术67. Malware Analysis - 恶意软件分析68. Online Privacy - 在线隐私69. Speech Recognition - 语音识别70. Cyber Forensics - 网络取证71. Data Anonymization - 数据匿名化72. IoT Platform - 物联网平台73. Network Infrastructure - 网络基础设施74. Predictive Analytics - 预测分析75. Software Development Tools - 软件开发工具77. Information Security Management - 信息安全管理78. Network Monitoring - 网络监控79. Software Deployment - 软件部署80. Data Encryption - 数据加密81. IoT Gateway - 物联网网关82. Network Topology - 网络拓扑结构83. Quantum Computing - 量子计算84. Software Configuration Management - 软件配置管理85. Data Lakes - 数据湖86. Infrastructure as a Service (IaaS) - 基础设施即服务87. Network Virtualization - 网络虚拟化88. Robotic Process Automation - 机器人流程自动化89. Software as a Service (SaaS) - 软件即服务90. Data Governance - 数据治理91. Information Security Policy - 信息安全政策92. Network Security Risk Assessment - 网络安全风险评估93. Secure Software Development - 安全软件开发94. Internet Security - 互联网安全95. Secure Coding Practices - 安全编码实践96. Secure Network Design - 安全网络设计97. Software Security Testing - 软件安全测试98. IoT Security Standards - 物联网安全标准99. Network Security Monitoring - 网络安全监控100. Vulnerability Management - 漏洞管理These terms cover a wide range of topics within the field of Information Engineering, and are essential in understanding and discussing the various aspects of this discipline. It is important for professionals in this field to be familiar with these terms in order to effectively communicate and collaborate with others in the industry.。
机器学习与人工智能领域中常用的英语词汇
机器学习与人工智能领域中常用的英语词汇1.General Concepts (基础概念)•Artificial Intelligence (AI) - 人工智能1)Artificial Intelligence (AI) - 人工智能2)Machine Learning (ML) - 机器学习3)Deep Learning (DL) - 深度学习4)Neural Network - 神经网络5)Natural Language Processing (NLP) - 自然语言处理6)Computer Vision - 计算机视觉7)Robotics - 机器人技术8)Speech Recognition - 语音识别9)Expert Systems - 专家系统10)Knowledge Representation - 知识表示11)Pattern Recognition - 模式识别12)Cognitive Computing - 认知计算13)Autonomous Systems - 自主系统14)Human-Machine Interaction - 人机交互15)Intelligent Agents - 智能代理16)Machine Translation - 机器翻译17)Swarm Intelligence - 群体智能18)Genetic Algorithms - 遗传算法19)Fuzzy Logic - 模糊逻辑20)Reinforcement Learning - 强化学习•Machine Learning (ML) - 机器学习1)Machine Learning (ML) - 机器学习2)Artificial Neural Network - 人工神经网络3)Deep Learning - 深度学习4)Supervised Learning - 有监督学习5)Unsupervised Learning - 无监督学习6)Reinforcement Learning - 强化学习7)Semi-Supervised Learning - 半监督学习8)Training Data - 训练数据9)Test Data - 测试数据10)Validation Data - 验证数据11)Feature - 特征12)Label - 标签13)Model - 模型14)Algorithm - 算法15)Regression - 回归16)Classification - 分类17)Clustering - 聚类18)Dimensionality Reduction - 降维19)Overfitting - 过拟合20)Underfitting - 欠拟合•Deep Learning (DL) - 深度学习1)Deep Learning - 深度学习2)Neural Network - 神经网络3)Artificial Neural Network (ANN) - 人工神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Autoencoder - 自编码器9)Generative Adversarial Network (GAN) - 生成对抗网络10)Transfer Learning - 迁移学习11)Pre-trained Model - 预训练模型12)Fine-tuning - 微调13)Feature Extraction - 特征提取14)Activation Function - 激活函数15)Loss Function - 损失函数16)Gradient Descent - 梯度下降17)Backpropagation - 反向传播18)Epoch - 训练周期19)Batch Size - 批量大小20)Dropout - 丢弃法•Neural Network - 神经网络1)Neural Network - 神经网络2)Artificial Neural Network (ANN) - 人工神经网络3)Deep Neural Network (DNN) - 深度神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Feedforward Neural Network - 前馈神经网络9)Multi-layer Perceptron (MLP) - 多层感知器10)Radial Basis Function Network (RBFN) - 径向基函数网络11)Hopfield Network - 霍普菲尔德网络12)Boltzmann Machine - 玻尔兹曼机13)Autoencoder - 自编码器14)Spiking Neural Network (SNN) - 脉冲神经网络15)Self-organizing Map (SOM) - 自组织映射16)Restricted Boltzmann Machine (RBM) - 受限玻尔兹曼机17)Hebbian Learning - 海比安学习18)Competitive Learning - 竞争学习19)Neuroevolutionary - 神经进化20)Neuron - 神经元•Algorithm - 算法1)Algorithm - 算法2)Supervised Learning Algorithm - 有监督学习算法3)Unsupervised Learning Algorithm - 无监督学习算法4)Reinforcement Learning Algorithm - 强化学习算法5)Classification Algorithm - 分类算法6)Regression Algorithm - 回归算法7)Clustering Algorithm - 聚类算法8)Dimensionality Reduction Algorithm - 降维算法9)Decision Tree Algorithm - 决策树算法10)Random Forest Algorithm - 随机森林算法11)Support Vector Machine (SVM) Algorithm - 支持向量机算法12)K-Nearest Neighbors (KNN) Algorithm - K近邻算法13)Naive Bayes Algorithm - 朴素贝叶斯算法14)Gradient Descent Algorithm - 梯度下降算法15)Genetic Algorithm - 遗传算法16)Neural Network Algorithm - 神经网络算法17)Deep Learning Algorithm - 深度学习算法18)Ensemble Learning Algorithm - 集成学习算法19)Reinforcement Learning Algorithm - 强化学习算法20)Metaheuristic Algorithm - 元启发式算法•Model - 模型1)Model - 模型2)Machine Learning Model - 机器学习模型3)Artificial Intelligence Model - 人工智能模型4)Predictive Model - 预测模型5)Classification Model - 分类模型6)Regression Model - 回归模型7)Generative Model - 生成模型8)Discriminative Model - 判别模型9)Probabilistic Model - 概率模型10)Statistical Model - 统计模型11)Neural Network Model - 神经网络模型12)Deep Learning Model - 深度学习模型13)Ensemble Model - 集成模型14)Reinforcement Learning Model - 强化学习模型15)Support Vector Machine (SVM) Model - 支持向量机模型16)Decision Tree Model - 决策树模型17)Random Forest Model - 随机森林模型18)Naive Bayes Model - 朴素贝叶斯模型19)Autoencoder Model - 自编码器模型20)Convolutional Neural Network (CNN) Model - 卷积神经网络模型•Dataset - 数据集1)Dataset - 数据集2)Training Dataset - 训练数据集3)Test Dataset - 测试数据集4)Validation Dataset - 验证数据集5)Balanced Dataset - 平衡数据集6)Imbalanced Dataset - 不平衡数据集7)Synthetic Dataset - 合成数据集8)Benchmark Dataset - 基准数据集9)Open Dataset - 开放数据集10)Labeled Dataset - 标记数据集11)Unlabeled Dataset - 未标记数据集12)Semi-Supervised Dataset - 半监督数据集13)Multiclass Dataset - 多分类数据集14)Feature Set - 特征集15)Data Augmentation - 数据增强16)Data Preprocessing - 数据预处理17)Missing Data - 缺失数据18)Outlier Detection - 异常值检测19)Data Imputation - 数据插补20)Metadata - 元数据•Training - 训练1)Training - 训练2)Training Data - 训练数据3)Training Phase - 训练阶段4)Training Set - 训练集5)Training Examples - 训练样本6)Training Instance - 训练实例7)Training Algorithm - 训练算法8)Training Model - 训练模型9)Training Process - 训练过程10)Training Loss - 训练损失11)Training Epoch - 训练周期12)Training Batch - 训练批次13)Online Training - 在线训练14)Offline Training - 离线训练15)Continuous Training - 连续训练16)Transfer Learning - 迁移学习17)Fine-Tuning - 微调18)Curriculum Learning - 课程学习19)Self-Supervised Learning - 自监督学习20)Active Learning - 主动学习•Testing - 测试1)Testing - 测试2)Test Data - 测试数据3)Test Set - 测试集4)Test Examples - 测试样本5)Test Instance - 测试实例6)Test Phase - 测试阶段7)Test Accuracy - 测试准确率8)Test Loss - 测试损失9)Test Error - 测试错误10)Test Metrics - 测试指标11)Test Suite - 测试套件12)Test Case - 测试用例13)Test Coverage - 测试覆盖率14)Cross-Validation - 交叉验证15)Holdout Validation - 留出验证16)K-Fold Cross-Validation - K折交叉验证17)Stratified Cross-Validation - 分层交叉验证18)Test Driven Development (TDD) - 测试驱动开发19)A/B Testing - A/B 测试20)Model Evaluation - 模型评估•Validation - 验证1)Validation - 验证2)Validation Data - 验证数据3)Validation Set - 验证集4)Validation Examples - 验证样本5)Validation Instance - 验证实例6)Validation Phase - 验证阶段7)Validation Accuracy - 验证准确率8)Validation Loss - 验证损失9)Validation Error - 验证错误10)Validation Metrics - 验证指标11)Cross-Validation - 交叉验证12)Holdout Validation - 留出验证13)K-Fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation - 留一法交叉验证16)Validation Curve - 验证曲线17)Hyperparameter Validation - 超参数验证18)Model Validation - 模型验证19)Early Stopping - 提前停止20)Validation Strategy - 验证策略•Supervised Learning - 有监督学习1)Supervised Learning - 有监督学习2)Label - 标签3)Feature - 特征4)Target - 目标5)Training Labels - 训练标签6)Training Features - 训练特征7)Training Targets - 训练目标8)Training Examples - 训练样本9)Training Instance - 训练实例10)Regression - 回归11)Classification - 分类12)Predictor - 预测器13)Regression Model - 回归模型14)Classifier - 分类器15)Decision Tree - 决策树16)Support Vector Machine (SVM) - 支持向量机17)Neural Network - 神经网络18)Feature Engineering - 特征工程19)Model Evaluation - 模型评估20)Overfitting - 过拟合21)Underfitting - 欠拟合22)Bias-Variance Tradeoff - 偏差-方差权衡•Unsupervised Learning - 无监督学习1)Unsupervised Learning - 无监督学习2)Clustering - 聚类3)Dimensionality Reduction - 降维4)Anomaly Detection - 异常检测5)Association Rule Learning - 关联规则学习6)Feature Extraction - 特征提取7)Feature Selection - 特征选择8)K-Means - K均值9)Hierarchical Clustering - 层次聚类10)Density-Based Clustering - 基于密度的聚类11)Principal Component Analysis (PCA) - 主成分分析12)Independent Component Analysis (ICA) - 独立成分分析13)T-distributed Stochastic Neighbor Embedding (t-SNE) - t分布随机邻居嵌入14)Gaussian Mixture Model (GMM) - 高斯混合模型15)Self-Organizing Maps (SOM) - 自组织映射16)Autoencoder - 自动编码器17)Latent Variable - 潜变量18)Data Preprocessing - 数据预处理19)Outlier Detection - 异常值检测20)Clustering Algorithm - 聚类算法•Reinforcement Learning - 强化学习1)Reinforcement Learning - 强化学习2)Agent - 代理3)Environment - 环境4)State - 状态5)Action - 动作6)Reward - 奖励7)Policy - 策略8)Value Function - 值函数9)Q-Learning - Q学习10)Deep Q-Network (DQN) - 深度Q网络11)Policy Gradient - 策略梯度12)Actor-Critic - 演员-评论家13)Exploration - 探索14)Exploitation - 开发15)Temporal Difference (TD) - 时间差分16)Markov Decision Process (MDP) - 马尔可夫决策过程17)State-Action-Reward-State-Action (SARSA) - 状态-动作-奖励-状态-动作18)Policy Iteration - 策略迭代19)Value Iteration - 值迭代20)Monte Carlo Methods - 蒙特卡洛方法•Semi-Supervised Learning - 半监督学习1)Semi-Supervised Learning - 半监督学习2)Labeled Data - 有标签数据3)Unlabeled Data - 无标签数据4)Label Propagation - 标签传播5)Self-Training - 自训练6)Co-Training - 协同训练7)Transudative Learning - 传导学习8)Inductive Learning - 归纳学习9)Manifold Regularization - 流形正则化10)Graph-based Methods - 基于图的方法11)Cluster Assumption - 聚类假设12)Low-Density Separation - 低密度分离13)Semi-Supervised Support Vector Machines (S3VM) - 半监督支持向量机14)Expectation-Maximization (EM) - 期望最大化15)Co-EM - 协同期望最大化16)Entropy-Regularized EM - 熵正则化EM17)Mean Teacher - 平均教师18)Virtual Adversarial Training - 虚拟对抗训练19)Tri-training - 三重训练20)Mix Match - 混合匹配•Feature - 特征1)Feature - 特征2)Feature Engineering - 特征工程3)Feature Extraction - 特征提取4)Feature Selection - 特征选择5)Input Features - 输入特征6)Output Features - 输出特征7)Feature Vector - 特征向量8)Feature Space - 特征空间9)Feature Representation - 特征表示10)Feature Transformation - 特征转换11)Feature Importance - 特征重要性12)Feature Scaling - 特征缩放13)Feature Normalization - 特征归一化14)Feature Encoding - 特征编码15)Feature Fusion - 特征融合16)Feature Dimensionality Reduction - 特征维度减少17)Continuous Feature - 连续特征18)Categorical Feature - 分类特征19)Nominal Feature - 名义特征20)Ordinal Feature - 有序特征•Label - 标签1)Label - 标签2)Labeling - 标注3)Ground Truth - 地面真值4)Class Label - 类别标签5)Target Variable - 目标变量6)Labeling Scheme - 标注方案7)Multi-class Labeling - 多类别标注8)Binary Labeling - 二分类标注9)Label Noise - 标签噪声10)Labeling Error - 标注错误11)Label Propagation - 标签传播12)Unlabeled Data - 无标签数据13)Labeled Data - 有标签数据14)Semi-supervised Learning - 半监督学习15)Active Learning - 主动学习16)Weakly Supervised Learning - 弱监督学习17)Noisy Label Learning - 噪声标签学习18)Self-training - 自训练19)Crowdsourcing Labeling - 众包标注20)Label Smoothing - 标签平滑化•Prediction - 预测1)Prediction - 预测2)Forecasting - 预测3)Regression - 回归4)Classification - 分类5)Time Series Prediction - 时间序列预测6)Forecast Accuracy - 预测准确性7)Predictive Modeling - 预测建模8)Predictive Analytics - 预测分析9)Forecasting Method - 预测方法10)Predictive Performance - 预测性能11)Predictive Power - 预测能力12)Prediction Error - 预测误差13)Prediction Interval - 预测区间14)Prediction Model - 预测模型15)Predictive Uncertainty - 预测不确定性16)Forecast Horizon - 预测时间跨度17)Predictive Maintenance - 预测性维护18)Predictive Policing - 预测式警务19)Predictive Healthcare - 预测性医疗20)Predictive Maintenance - 预测性维护•Classification - 分类1)Classification - 分类2)Classifier - 分类器3)Class - 类别4)Classify - 对数据进行分类5)Class Label - 类别标签6)Binary Classification - 二元分类7)Multiclass Classification - 多类分类8)Class Probability - 类别概率9)Decision Boundary - 决策边界10)Decision Tree - 决策树11)Support Vector Machine (SVM) - 支持向量机12)K-Nearest Neighbors (KNN) - K最近邻算法13)Naive Bayes - 朴素贝叶斯14)Logistic Regression - 逻辑回归15)Random Forest - 随机森林16)Neural Network - 神经网络17)SoftMax Function - SoftMax函数18)One-vs-All (One-vs-Rest) - 一对多(一对剩余)19)Ensemble Learning - 集成学习20)Confusion Matrix - 混淆矩阵•Regression - 回归1)Regression Analysis - 回归分析2)Linear Regression - 线性回归3)Multiple Regression - 多元回归4)Polynomial Regression - 多项式回归5)Logistic Regression - 逻辑回归6)Ridge Regression - 岭回归7)Lasso Regression - Lasso回归8)Elastic Net Regression - 弹性网络回归9)Regression Coefficients - 回归系数10)Residuals - 残差11)Ordinary Least Squares (OLS) - 普通最小二乘法12)Ridge Regression Coefficient - 岭回归系数13)Lasso Regression Coefficient - Lasso回归系数14)Elastic Net Regression Coefficient - 弹性网络回归系数15)Regression Line - 回归线16)Prediction Error - 预测误差17)Regression Model - 回归模型18)Nonlinear Regression - 非线性回归19)Generalized Linear Models (GLM) - 广义线性模型20)Coefficient of Determination (R-squared) - 决定系数21)F-test - F检验22)Homoscedasticity - 同方差性23)Heteroscedasticity - 异方差性24)Autocorrelation - 自相关25)Multicollinearity - 多重共线性26)Outliers - 异常值27)Cross-validation - 交叉验证28)Feature Selection - 特征选择29)Feature Engineering - 特征工程30)Regularization - 正则化2.Neural Networks and Deep Learning (神经网络与深度学习)•Convolutional Neural Network (CNN) - 卷积神经网络1)Convolutional Neural Network (CNN) - 卷积神经网络2)Convolution Layer - 卷积层3)Feature Map - 特征图4)Convolution Operation - 卷积操作5)Stride - 步幅6)Padding - 填充7)Pooling Layer - 池化层8)Max Pooling - 最大池化9)Average Pooling - 平均池化10)Fully Connected Layer - 全连接层11)Activation Function - 激活函数12)Rectified Linear Unit (ReLU) - 线性修正单元13)Dropout - 随机失活14)Batch Normalization - 批量归一化15)Transfer Learning - 迁移学习16)Fine-Tuning - 微调17)Image Classification - 图像分类18)Object Detection - 物体检测19)Semantic Segmentation - 语义分割20)Instance Segmentation - 实例分割21)Generative Adversarial Network (GAN) - 生成对抗网络22)Image Generation - 图像生成23)Style Transfer - 风格迁移24)Convolutional Autoencoder - 卷积自编码器25)Recurrent Neural Network (RNN) - 循环神经网络•Recurrent Neural Network (RNN) - 循环神经网络1)Recurrent Neural Network (RNN) - 循环神经网络2)Long Short-Term Memory (LSTM) - 长短期记忆网络3)Gated Recurrent Unit (GRU) - 门控循环单元4)Sequence Modeling - 序列建模5)Time Series Prediction - 时间序列预测6)Natural Language Processing (NLP) - 自然语言处理7)Text Generation - 文本生成8)Sentiment Analysis - 情感分析9)Named Entity Recognition (NER) - 命名实体识别10)Part-of-Speech Tagging (POS Tagging) - 词性标注11)Sequence-to-Sequence (Seq2Seq) - 序列到序列12)Attention Mechanism - 注意力机制13)Encoder-Decoder Architecture - 编码器-解码器架构14)Bidirectional RNN - 双向循环神经网络15)Teacher Forcing - 强制教师法16)Backpropagation Through Time (BPTT) - 通过时间的反向传播17)Vanishing Gradient Problem - 梯度消失问题18)Exploding Gradient Problem - 梯度爆炸问题19)Language Modeling - 语言建模20)Speech Recognition - 语音识别•Long Short-Term Memory (LSTM) - 长短期记忆网络1)Long Short-Term Memory (LSTM) - 长短期记忆网络2)Cell State - 细胞状态3)Hidden State - 隐藏状态4)Forget Gate - 遗忘门5)Input Gate - 输入门6)Output Gate - 输出门7)Peephole Connections - 窥视孔连接8)Gated Recurrent Unit (GRU) - 门控循环单元9)Vanishing Gradient Problem - 梯度消失问题10)Exploding Gradient Problem - 梯度爆炸问题11)Sequence Modeling - 序列建模12)Time Series Prediction - 时间序列预测13)Natural Language Processing (NLP) - 自然语言处理14)Text Generation - 文本生成15)Sentiment Analysis - 情感分析16)Named Entity Recognition (NER) - 命名实体识别17)Part-of-Speech Tagging (POS Tagging) - 词性标注18)Attention Mechanism - 注意力机制19)Encoder-Decoder Architecture - 编码器-解码器架构20)Bidirectional LSTM - 双向长短期记忆网络•Attention Mechanism - 注意力机制1)Attention Mechanism - 注意力机制2)Self-Attention - 自注意力3)Multi-Head Attention - 多头注意力4)Transformer - 变换器5)Query - 查询6)Key - 键7)Value - 值8)Query-Value Attention - 查询-值注意力9)Dot-Product Attention - 点积注意力10)Scaled Dot-Product Attention - 缩放点积注意力11)Additive Attention - 加性注意力12)Context Vector - 上下文向量13)Attention Score - 注意力分数14)SoftMax Function - SoftMax函数15)Attention Weight - 注意力权重16)Global Attention - 全局注意力17)Local Attention - 局部注意力18)Positional Encoding - 位置编码19)Encoder-Decoder Attention - 编码器-解码器注意力20)Cross-Modal Attention - 跨模态注意力•Generative Adversarial Network (GAN) - 生成对抗网络1)Generative Adversarial Network (GAN) - 生成对抗网络2)Generator - 生成器3)Discriminator - 判别器4)Adversarial Training - 对抗训练5)Minimax Game - 极小极大博弈6)Nash Equilibrium - 纳什均衡7)Mode Collapse - 模式崩溃8)Training Stability - 训练稳定性9)Loss Function - 损失函数10)Discriminative Loss - 判别损失11)Generative Loss - 生成损失12)Wasserstein GAN (WGAN) - Wasserstein GAN（WGAN）13)Deep Convolutional GAN (DCGAN) - 深度卷积生成对抗网络（DCGAN）14)Conditional GAN (c GAN) - 条件生成对抗网络（c GAN）15)Style GAN - 风格生成对抗网络16)Cycle GAN - 循环生成对抗网络17)Progressive Growing GAN (PGGAN) - 渐进式增长生成对抗网络（PGGAN）18)Self-Attention GAN (SAGAN) - 自注意力生成对抗网络（SAGAN）19)Big GAN - 大规模生成对抗网络20)Adversarial Examples - 对抗样本•Encoder-Decoder - 编码器-解码器1)Encoder-Decoder Architecture - 编码器-解码器架构2)Encoder - 编码器3)Decoder - 解码器4)Sequence-to-Sequence Model (Seq2Seq) - 序列到序列模型5)State Vector - 状态向量6)Context Vector - 上下文向量7)Hidden State - 隐藏状态8)Attention Mechanism - 注意力机制9)Teacher Forcing - 强制教师法10)Beam Search - 束搜索11)Recurrent Neural Network (RNN) - 循环神经网络12)Long Short-Term Memory (LSTM) - 长短期记忆网络13)Gated Recurrent Unit (GRU) - 门控循环单元14)Bidirectional Encoder - 双向编码器15)Greedy Decoding - 贪婪解码16)Masking - 遮盖17)Dropout - 随机失活18)Embedding Layer - 嵌入层19)Cross-Entropy Loss - 交叉熵损失20)Tokenization - 令牌化•Transfer Learning - 迁移学习1)Transfer Learning - 迁移学习2)Source Domain - 源领域3)Target Domain - 目标领域4)Fine-Tuning - 微调5)Domain Adaptation - 领域自适应6)Pre-Trained Model - 预训练模型7)Feature Extraction - 特征提取8)Knowledge Transfer - 知识迁移9)Unsupervised Domain Adaptation - 无监督领域自适应10)Semi-Supervised Domain Adaptation - 半监督领域自适应11)Multi-Task Learning - 多任务学习12)Data Augmentation - 数据增强13)Task Transfer - 任务迁移14)Model Agnostic Meta-Learning (MAML) - 与模型无关的元学习（MAML）15)One-Shot Learning - 单样本学习16)Zero-Shot Learning - 零样本学习17)Few-Shot Learning - 少样本学习18)Knowledge Distillation - 知识蒸馏19)Representation Learning - 表征学习20)Adversarial Transfer Learning - 对抗迁移学习•Pre-trained Models - 预训练模型1)Pre-trained Model - 预训练模型2)Transfer Learning - 迁移学习3)Fine-Tuning - 微调4)Knowledge Transfer - 知识迁移5)Domain Adaptation - 领域自适应6)Feature Extraction - 特征提取7)Representation Learning - 表征学习8)Language Model - 语言模型9)Bidirectional Encoder Representations from Transformers (BERT) - 双向编码器结构转换器10)Generative Pre-trained Transformer (GPT) - 生成式预训练转换器11)Transformer-based Models - 基于转换器的模型12)Masked Language Model (MLM) - 掩蔽语言模型13)Cloze Task - 填空任务14)Tokenization - 令牌化15)Word Embeddings - 词嵌入16)Sentence Embeddings - 句子嵌入17)Contextual Embeddings - 上下文嵌入18)Self-Supervised Learning - 自监督学习19)Large-Scale Pre-trained Models - 大规模预训练模型•Loss Function - 损失函数1)Loss Function - 损失函数2)Mean Squared Error (MSE) - 均方误差3)Mean Absolute Error (MAE) - 平均绝对误差4)Cross-Entropy Loss - 交叉熵损失5)Binary Cross-Entropy Loss - 二元交叉熵损失6)Categorical Cross-Entropy Loss - 分类交叉熵损失7)Hinge Loss - 合页损失8)Huber Loss - Huber损失9)Wasserstein Distance - Wasserstein距离10)Triplet Loss - 三元组损失11)Contrastive Loss - 对比损失12)Dice Loss - Dice损失13)Focal Loss - 焦点损失14)GAN Loss - GAN损失15)Adversarial Loss - 对抗损失16)L1 Loss - L1损失17)L2 Loss - L2损失18)Huber Loss - Huber损失19)Quantile Loss - 分位数损失•Activation Function - 激活函数1)Activation Function - 激活函数2)Sigmoid Function - Sigmoid函数3)Hyperbolic Tangent Function (Tanh) - 双曲正切函数4)Rectified Linear Unit (Re LU) - 矩形线性单元5)Parametric Re LU (P Re LU) - 参数化Re LU6)Exponential Linear Unit (ELU) - 指数线性单元7)Swish Function - Swish函数8)Softplus Function - Soft plus函数9)Softmax Function - SoftMax函数10)Hard Tanh Function - 硬双曲正切函数11)Softsign Function - Softsign函数12)GELU (Gaussian Error Linear Unit) - GELU（高斯误差线性单元）13)Mish Function - Mish函数14)CELU (Continuous Exponential Linear Unit) - CELU（连续指数线性单元）15)Bent Identity Function - 弯曲恒等函数16)Gaussian Error Linear Units (GELUs) - 高斯误差线性单元17)Adaptive Piecewise Linear (APL) - 自适应分段线性函数18)Radial Basis Function (RBF) - 径向基函数•Backpropagation - 反向传播1)Backpropagation - 反向传播2)Gradient Descent - 梯度下降3)Partial Derivative - 偏导数4)Chain Rule - 链式法则5)Forward Pass - 前向传播6)Backward Pass - 反向传播7)Computational Graph - 计算图8)Neural Network - 神经网络9)Loss Function - 损失函数10)Gradient Calculation - 梯度计算11)Weight Update - 权重更新12)Activation Function - 激活函数13)Optimizer - 优化器14)Learning Rate - 学习率15)Mini-Batch Gradient Descent - 小批量梯度下降16)Stochastic Gradient Descent (SGD) - 随机梯度下降17)Batch Gradient Descent - 批量梯度下降18)Momentum - 动量19)Adam Optimizer - Adam优化器20)Learning Rate Decay - 学习率衰减•Gradient Descent - 梯度下降1)Gradient Descent - 梯度下降2)Stochastic Gradient Descent (SGD) - 随机梯度下降3)Mini-Batch Gradient Descent - 小批量梯度下降4)Batch Gradient Descent - 批量梯度下降5)Learning Rate - 学习率6)Momentum - 动量7)Adaptive Moment Estimation (Adam) - 自适应矩估计8)RMSprop - 均方根传播9)Learning Rate Schedule - 学习率调度10)Convergence - 收敛11)Divergence - 发散12)Adagrad - 自适应学习速率方法13)Adadelta - 自适应增量学习率方法14)Adamax - 自适应矩估计的扩展版本15)Nadam - Nesterov Accelerated Adaptive Moment Estimation16)Learning Rate Decay - 学习率衰减17)Step Size - 步长18)Conjugate Gradient Descent - 共轭梯度下降19)Line Search - 线搜索20)Newton's Method - 牛顿法•Learning Rate - 学习率1)Learning Rate - 学习率2)Adaptive Learning Rate - 自适应学习率3)Learning Rate Decay - 学习率衰减4)Initial Learning Rate - 初始学习率5)Step Size - 步长6)Momentum - 动量7)Exponential Decay - 指数衰减8)Annealing - 退火9)Cyclical Learning Rate - 循环学习率10)Learning Rate Schedule - 学习率调度11)Warm-up - 预热12)Learning Rate Policy - 学习率策略13)Learning Rate Annealing - 学习率退火14)Cosine Annealing - 余弦退火15)Gradient Clipping - 梯度裁剪16)Adapting Learning Rate - 适应学习率17)Learning Rate Multiplier - 学习率倍增器18)Learning Rate Reduction - 学习率降低19)Learning Rate Update - 学习率更新20)Scheduled Learning Rate - 定期学习率•Batch Size - 批量大小1)Batch Size - 批量大小2)Mini-Batch - 小批量3)Batch Gradient Descent - 批量梯度下降4)Stochastic Gradient Descent (SGD) - 随机梯度下降5)Mini-Batch Gradient Descent - 小批量梯度下降6)Online Learning - 在线学习7)Full-Batch - 全批量8)Data Batch - 数据批次9)Training Batch - 训练批次10)Batch Normalization - 批量归一化11)Batch-wise Optimization - 批量优化12)Batch Processing - 批量处理13)Batch Sampling - 批量采样14)Adaptive Batch Size - 自适应批量大小15)Batch Splitting - 批量分割16)Dynamic Batch Size - 动态批量大小17)Fixed Batch Size - 固定批量大小18)Batch-wise Inference - 批量推理19)Batch-wise Training - 批量训练20)Batch Shuffling - 批量洗牌•Epoch - 训练周期1)Training Epoch - 训练周期2)Epoch Size - 周期大小3)Early Stopping - 提前停止4)Validation Set - 验证集5)Training Set - 训练集6)Test Set - 测试集7)Overfitting - 过拟合8)Underfitting - 欠拟合9)Model Evaluation - 模型评估10)Model Selection - 模型选择11)Hyperparameter Tuning - 超参数调优12)Cross-Validation - 交叉验证13)K-fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation (LOOCV) - 留一法交叉验证16)Grid Search - 网格搜索17)Random Search - 随机搜索18)Model Complexity - 模型复杂度19)Learning Curve - 学习曲线20)Convergence - 收敛3.Machine Learning Techniques and Algorithms (机器学习技术与算法)•Decision Tree - 决策树1)Decision Tree - 决策树2)Node - 节点3)Root Node - 根节点4)Leaf Node - 叶节点5)Internal Node - 内部节点6)Splitting Criterion - 分裂准则7)Gini Impurity - 基尼不纯度8)Entropy - 熵9)Information Gain - 信息增益10)Gain Ratio - 增益率11)Pruning - 剪枝12)Recursive Partitioning - 递归分割13)CART (Classification and Regression Trees) - 分类回归树14)ID3 (Iterative Dichotomiser 3) - 迭代二叉树315)C4.5 (successor of ID3) - C4.5（ID3的后继者）16)C5.0 (successor of C4.5) - C5.0（C4.5的后继者）17)Split Point - 分裂点18)Decision Boundary - 决策边界19)Pruned Tree - 剪枝后的树20)Decision Tree Ensemble - 决策树集成•Random Forest - 随机森林1)Random Forest - 随机森林2)Ensemble Learning - 集成学习3)Bootstrap Sampling - 自助采样4)Bagging (Bootstrap Aggregating) - 装袋法5)Out-of-Bag (OOB) Error - 袋外误差6)Feature Subset - 特征子集7)Decision Tree - 决策树8)Base Estimator - 基础估计器9)Tree Depth - 树深度10)Randomization - 随机化11)Majority Voting - 多数投票12)Feature Importance - 特征重要性13)OOB Score - 袋外得分14)Forest Size - 森林大小15)Max Features - 最大特征数16)Min Samples Split - 最小分裂样本数17)Min Samples Leaf - 最小叶节点样本数18)Gini Impurity - 基尼不纯度19)Entropy - 熵20)Variable Importance - 变量重要性•Support Vector Machine (SVM) - 支持向量机1)Support Vector Machine (SVM) - 支持向量机2)Hyperplane - 超平面3)Kernel Trick - 核技巧4)Kernel Function - 核函数5)Margin - 间隔6)Support Vectors - 支持向量7)Decision Boundary - 决策边界8)Maximum Margin Classifier - 最大间隔分类器9)Soft Margin Classifier - 软间隔分类器10) C Parameter - C参数11)Radial Basis Function (RBF) Kernel - 径向基函数核12)Polynomial Kernel - 多项式核13)Linear Kernel - 线性核14)Quadratic Kernel - 二次核15)Gaussian Kernel - 高斯核16)Regularization - 正则化17)Dual Problem - 对偶问题18)Primal Problem - 原始问题19)Kernelized SVM - 核化支持向量机20)Multiclass SVM - 多类支持向量机•K-Nearest Neighbors (KNN) - K-最近邻1)K-Nearest Neighbors (KNN) - K-最近邻2)Nearest Neighbor - 最近邻3)Distance Metric - 距离度量4)Euclidean Distance - 欧氏距离5)Manhattan Distance - 曼哈顿距离6)Minkowski Distance - 闵可夫斯基距离7)Cosine Similarity - 余弦相似度8)K Value - K值9)Majority Voting - 多数投票10)Weighted KNN - 加权KNN11)Radius Neighbors - 半径邻居12)Ball Tree - 球树13)KD Tree - KD树14)Locality-Sensitive Hashing (LSH) - 局部敏感哈希15)Curse of Dimensionality - 维度灾难16)Class Label - 类标签17)Training Set - 训练集18)Test Set - 测试集19)Validation Set - 验证集20)Cross-Validation - 交叉验证•Naive Bayes - 朴素贝叶斯1)Naive Bayes - 朴素贝叶斯2)Bayes' Theorem - 贝叶斯定理3)Prior Probability - 先验概率4)Posterior Probability - 后验概率5)Likelihood - 似然6)Class Conditional Probability - 类条件概率7)Feature Independence Assumption - 特征独立假设8)Multinomial Naive Bayes - 多项式朴素贝叶斯9)Gaussian Naive Bayes - 高斯朴素贝叶斯10)Bernoulli Naive Bayes - 伯努利朴素贝叶斯11)Laplace Smoothing - 拉普拉斯平滑12)Add-One Smoothing - 加一平滑13)Maximum A Posteriori (MAP) - 最大后验概率14)Maximum Likelihood Estimation (MLE) - 最大似然估计15)Classification - 分类16)Feature Vectors - 特征向量17)Training Set - 训练集18)Test Set - 测试集19)Class Label - 类标签20)Confusion Matrix - 混淆矩阵•Clustering - 聚类1)Clustering - 聚类2)Centroid - 质心3)Cluster Analysis - 聚类分析4)Partitioning Clustering - 划分式聚类5)Hierarchical Clustering - 层次聚类6)Density-Based Clustering - 基于密度的聚类7)K-Means Clustering - K均值聚类8)K-Medoids Clustering - K中心点聚类9)DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - 基于密度的空间聚类算法10)Agglomerative Clustering - 聚合式聚类11)Dendrogram - 系统树图12)Silhouette Score - 轮廓系数13)Elbow Method - 肘部法则14)Clustering Validation - 聚类验证15)Intra-cluster Distance - 类内距离16)Inter-cluster Distance - 类间距离17)Cluster Cohesion - 类内连贯性18)Cluster Separation - 类间分离度19)Cluster Assignment - 聚类分配20)Cluster Label - 聚类标签•K-Means - K-均值1)K-Means - K-均值2)Centroid - 质心3)Cluster - 聚类4)Cluster Center - 聚类中心5)Cluster Assignment - 聚类分配6)Cluster Analysis - 聚类分析7)K Value - K值8)Elbow Method - 肘部法则9)Inertia - 惯性10)Silhouette Score - 轮廓系数11)Convergence - 收敛12)Initialization - 初始化13)Euclidean Distance - 欧氏距离14)Manhattan Distance - 曼哈顿距离15)Distance Metric - 距离度量16)Cluster Radius - 聚类半径17)Within-Cluster Variation - 类内变异18)Cluster Quality - 聚类质量19)Clustering Algorithm - 聚类算法20)Clustering Validation - 聚类验证•Dimensionality Reduction - 降维1)Dimensionality Reduction - 降维2)Feature Extraction - 特征提取3)Feature Selection - 特征选择4)Principal Component Analysis (PCA) - 主成分分析5)Singular Value Decomposition (SVD) - 奇异值分解6)Linear Discriminant Analysis (LDA) - 线性判别分析7)t-Distributed Stochastic Neighbor Embedding (t-SNE) - t-分布随机邻域嵌入8)Autoencoder - 自编码器9)Manifold Learning - 流形学习10)Locally Linear Embedding (LLE) - 局部线性嵌入11)Isomap - 等度量映射12)Uniform Manifold Approximation and Projection (UMAP) - 均匀流形逼近与投影13)Kernel PCA - 核主成分分析14)Non-negative Matrix Factorization (NMF) - 非负矩阵分解15)Independent Component Analysis (ICA) - 独立成分分析16)Variational Autoencoder (VAE) - 变分自编码器17)Sparse Coding - 稀疏编码18)Random Projection - 随机投影19)Neighborhood Preserving Embedding (NPE) - 保持邻域结构的嵌入20)Curvilinear Component Analysis (CCA) - 曲线成分分析•Principal Component Analysis (PCA) - 主成分分析1)Principal Component Analysis (PCA) - 主成分分析2)Eigenvector - 特征向量3)Eigenvalue - 特征值4)Covariance Matrix - 协方差矩阵。
企业技术开发名词解释
企业技术开发名词解释企业技术开发是指企业利用技术手段开展新产品、新技术或新业务的研发和实施过程。
以下是相关名词解释：1. 技术研发（Research and Development，简称R&D）：企业通过投入人力、物力和财力进行技术创新和研发活动，以寻找新的技术解决方案或改进现有技术。
2. 创新（Innovation）：指引入新的理念、方法、产品、服务或组织形式，以提升企业竞争力和市场地位。
3. 技术推广（Technology Transfer）：将企业的技术成果、专利或知识转移到其他企业或组织，以实现技术的应用和商业化。
4. 技术孵化器（Technology Incubator）：提供物质、知识和资金支持给初创企业进行技术开发和商业化的组织或机构。
5. 创新生态圈（Innovation Ecosystem）：由各类企业、科研机构、大学、投资机构等组成的一个生态系统，通过合作和交流促进创新和技术开发。
6. 专利（Patent）：对发明、技术或设计的独占权，确保企业的技术创新能够得到法律保护和经济回报。
7. 技术成果转化（Technology Commercialization）：将技术研发成果转变为市场可行的产品或服务的过程，以实现经济效益。
8. 敏捷开发（Agile Development）：一种软件开发方法论，以迭代、适应和快速响应需求变化为特点，提高软件开发效率和质量。
9. 产品生命周期管理（Product Lifecycle Management，简称PLM）：在产品从概念到退市的整个生命周期中，对产品信息和流程进行管理和协同。
10. 人工智能（Artificial Intelligence，简称AI）：指计算机系统通过模拟人类智能的方式实现的技术，包括机器学习、自然语言处理和计算机视觉等领域。
11. 云计算（Cloud Computing）：一种基于互联网的计算方式，通过网络提供计算资源和服务，使企业能够实现按需获取、灵活扩展和节约成本的计算能力。
Artificial Intelligence 第一章 人工智能的基本概念(导论) 《人工智能》课件
第三节 人工智能的研究目标
AI的研究目标分近期目标和远期目标：
近期目标：研究如何使计算机去做那些过去只有靠
人的智力才能完成的工作。
远期目标：研究如何利用自动机去模拟人的某些思
可用模型 进行评价
2.智能的要素：
最重要的要素包括：适应环境、适应偶然性事件、能分 辩模糊的或矛盾的信息，在孤立的情况中找出相似性，产生新 概念和新思想。
3.智能的分类：
自然智能 有规律的智能行为：计算机能解决
人工智能 无规律的智能行为：如洞察力、创造力。 关于这些问题：计算机还不能解决。
三、如何判定智能？
第五节 AI的发展简史
第一阶段：孕育期（1956年以前） 第 二 阶 段 ： AI 的 基 础 技 术 的 研 究 和 形 成 时 期 1956— 1970 第 三 阶 段 ： AI 发 展 和 实 用 阶 段 （ 专 家 系 统 ） 1971— 1980 第四阶段：知识工程与机器学习发展阶段1981—1990 第五阶段：智能综合集成阶段,二十世纪90年代至今，
英国自然杂志主编坎贝尔博士说：目前信息技术和生命科学 有交叉融合的趋势，比如AI的研究就需要从生命科学的角度揭 开大脑思维的机理，需要利用信息技术模拟实现这种机理。 （参考文献：李凡长、佘玉梅：Agent的遗传算法研究，《计 算机科学》）
3.行为主义（Actionism）：
又 称 进 化 主 义 （ Evolutionism ） 或 控 制 论 学 派 （Cyberneticisism）。其原理为控制论及感知再到动作型控 制系统。主要进行行为模拟，代表人物：布鲁克斯等。
agi是什么意思
agi是什么意思AGI是ArtificialGeneralIntelligence（人工一般智能）的简称，也可以说是通用人工智能（General Artificial Intelligence）或超级人工智能（Super Artificial Intelligence），它是一种可以执行复杂任务的人工智能，能够完全模仿人类智能的行为。
AGI可以被认为是AI（人工智能）的更高层次，它可以实现自我学习，自我改进，自我调整，进而解决任何问题而不需要人为干预。
AGI的另一种称呼“人工一般智能”，可以从两个方面来解释：“一般”指的是AGI的能力能够达到和人类相同的智力水平；“智能”指的是AGI可以达到人类以上的智力水平，并且可以比人类更快、更准确地完成各种任务。
AGI技术可以被广泛应用于机器视觉、机器学习、自然语言处理、增强学习等各个领域，所有这些技术在结合现实世界的复杂性和难以了解的动态性时，都将有助于AGI取得巨大的进步。
AGI能进行更深层次的思考，且基于各种经验总结出适用于更大规模、更丰富内容的结论，而且有能力适应新情况，它也可以为企业带来更多的巨大福利，帮助企业提升效率，降低运营成本，更好地满足客户需求，增强竞争实力。
此外，AGI也可以在医疗保健领域有着重要作用，可以大大提高诊断和治疗的准确性，减少误诊和漏诊的概率，更快、更准确地诊断各种疾病，为病人提供更精准的疗程，使他们能够得到更优质的护理服务。
当前，AGI已经取得显著的发展，但也仍处于发展的初级阶段，实现全面智能仍然面临着挑战，需要在数据采集、模型训练、推理优化等方面大力投入研究和开发，从而推动AGI技术取得真正的突破。
总而言之，AGI是一种人工智能技术，其具有一般智能和超级智能的特征，能够适应复杂性和多变性，实现自我学习和自我改进，可以在各个领域大大提高效率，并为人类带来有益的影响。
agi是什么意思
agi是什么意思AGI是“ArtificialGeneralIntelligence”的缩写，即人工智能（Artificial Intelligence，AI）的一种，也是当前AI研究的前沿课题。
它的定义是：人工智能可以自然地、像人类一样进行解决问题的智能，而不是某一项特殊问题的智能。
AGI的特点是具有人类智力水平，可以进行多种类型的任务，而不仅限于特定任务或特定环境。
并且不仅限于专家系统，它可以实现解答可变性，分析复杂数据，解决新问题，模仿人类思维等多种智力行为。
AGI具有以下重要特征：1、全面性：AGI包括了计算机算法、知识抽取、概念/语义空间的表示、信息处理、机器学习等方面的内容，使计算机能够实现自主性和适应性。
2、学习性： AGI可以参考多种数据来学习，通过模拟学习，自动调整参数和算法，从而可以获得良好的智能表现。
3、智能化： AGI包括计算机视觉、自然语言理解、自动驾驶、虚拟机器人等综合性技术，可以实现自动解决复杂任务，同时也可以应用于获取经验和学习。
AGI的发展可以从以下几个方面来分析：1、硬件：提高运算能力和存储能力，以及开发新的芯片，将加快人工智能的发展。
2、模型：利用深度学习和机器学习技术，改进计算机算法，以更好地理解和推理复杂数据，并获得良好的结果。
3、数据：通过建立大量的数据库，可以提高计算机对复杂环境的模拟能力，增强机器思考。
4、应用：AGI可以应用于各种领域，从医疗诊断、复杂机械控制到自动语音识别，都能得到良好的推理效果。
AGI的发展将带来无数的可能性，它可以在金融、医疗、机器人、自动驾驶汽车、制造业、军事等多个领域起到支撑作用。
通过人工智能技术，人类可以实现劳动力外置、预测现象变化，同时AGI也会完善人类的生活，改善人们的社会状况。
AGI的发展也会带来很多问题，比如隐私保护、去中心化的技术、流程优化等。
在未来的研究中，必须要注重安全和保护性问题，发挥技术优势，推动AGI的可持续发展。
技术流派的名词解释
技术流派的名词解释在现代社会中，技术的发展日新月异。
各行各业都有自己的技术流派，这些流派涵盖了不同的理论、方法和实践。
本文将就几个常见的技术流派进行解释和讨论，以帮助读者更好地理解和应用这些技术。
1. 敏捷开发（Agile Development）敏捷开发是一种以灵活和迭代的方式进行软件开发的方法。
它的核心是快速响应变化和持续交付价值。
与传统的瀑布式开发相比，敏捷开发更加注重团队协作、频繁交付可工作软件和快速反馈。
敏捷开发的流派包括Scrum、XP和Lean等。
2. 人工智能（Artificial Intelligence）人工智能是模拟人类智能的一种技术。
它通过机器学习、深度学习和自然语言处理等技术，使计算机具备学习和推理能力。
人工智能可以应用于各个领域，如自动驾驶、语音识别和智能机器人等。
不同的人工智能流派包括强化学习、神经网络和专家系统等。
3. 云计算（Cloud Computing）云计算是一种使用互联网进行数据存储和处理的技术。
它将计算资源、存储空间和应用程序等通过网络提供给用户，以高效地满足用户的需求。
云计算流派包括基础设施即服务（IaaS）、平台即服务（PaaS）和软件即服务（SaaS）等。
4. 区块链（Blockchain）区块链是一种去中心化的分布式账本技术。
它通过将交易记录以块的形式链接在一起，并通过共识算法确保每个节点上的账本保持一致。
区块链可以应用于数字货币、智能合约和供应链管理等领域。
不同的区块链流派包括比特币、以太坊和超级账本等。
5. 虚拟现实（Virtual Reality）虚拟现实是一种模拟现实环境的计算机生成技术。
通过戴上特定的头戴式设备，用户可以感受到身临其境的视觉和听觉体验。
虚拟现实可以应用于游戏、教育和医疗等领域。
不同的虚拟现实流派包括增强现实和混合现实等。
6. 物联网（Internet of Things）物联网是一种将物理设备与互联网连接的技术。
通过传感器、通信设备和云计算等技术，物联网可以实现设备之间的互联互通和远程控制。
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Demonstrations of Information Consumption
Leverage the Spine of the Information
Tables Time lines Networks and Influence (graphs) Geo-Spatial/Proximity Multivariate data – know human limits (5-7 dimensions)
+ Structure
Intelligence Cycle MS Project
SharePoint Portal Server Content Management Server IM Exchange
Disseminate Action! Plan Collect
BizTalk HIS Web Services SQL Server Analysis Services
Data Processing
1960
Online Processing
Enterprise Computing
2000
What is BI?
Assisting Human Decision Making with Technology
Any other definition is too narrow Example: Who, what, where, when, how, and the most difficult, why? Internal and External sources It is not better reporting (“Shelf-ware” 69% goes unused) (“ShelfIt’s not an IT project It’s not a Business project It is a process and it is never done
Approx 2ms Approx 2ms
Hardware is cheap – people are expensive!
Cost of a raw 1 TB of disk Cost to manage 1 TB of disk
$1K $300K / year
Evolution of self-configuration
Source Systems
XML/A
Query Tools Reporting Analysis Data Mining
1 2 3 4
Design the Data Warehouse
Populate Data Warehouse
Create OLAP Cubes
Query Data
Why Cube the data? Approachability and Speed.
Reduce the time it takes to perform each step Reduce the number of steps Reduce the latency between steps
First 3 Waves of Database
ERP
Automating Process Steps Example: Payroll Reducing Process Steps Online Query & Reporting Automating Process Steps Example: ERP, HR, CRM
Strategic Value of Decisions
Executives
Analysts
Middle Management
Operations Number of Decisions Made
Building The Warehouse
Elements of the process
Data Marts and cubes Data Warehouse Clients & Web Services
What Definition? Transactions/Min, Concurrent Users, Volume, Query Response
Database and Integration Engine Blur (XML & EAI & B2B & ETL – NRT/RT) Business Intelligence – the final frontier – Intelligence Engineering, not database engineering. Increased Bundling to deal with above
Welcome
Agile Intelligence
with Microsoft Technology
Sam Batterman (samb@) Microsoft Corporation Greater PA – Malvern, PA
Some general database trends…
These Slides are available electronically at:
/usa/presentations/search.asp?district=greaterpa Agile Intelligence - Agile Intelligence - HFMA Conference April 22, 2003
Trends Hardware keeps getting more powerful
CPU Bandwidth Disk Capacity Disk I/O Latency Network Latency 2X / 18 months 3X / 18 months 100X in 10 years
Turning Data into Wisdom
Data Informationห้องสมุดไป่ตู้Knowledge Wisdom
+Interaction
Query Filter Highlight Track Trace Isolate Group & Catalog Proximity Rank Common Behavior Compare & Contrast Anomaly
Emerging Technology • Lots of knobs • High maintenance – testing, replacing tubes, tuner cleaning, etc. Some people actually enjoyed tinkering… Refining Technology • Automatic color/tint, (or you could control it) • Didn’t have to fool with vertical hold • Much less maintenance (transistors vs. tubes) • “Instant on” Big improvement – good enough for masses Refined Technology • No knobs – “vertical hold…what’s that?” • Highly reliable and available • Goal directed input – mine has control for watching, movies, sports, etc. “It just works”
Acceptance and Growth of “feature parity” databases Hard Drive Acreage becoming so cheap and economical that tape backup is too troublesome for every day Memory becoming so vast and cheap that many typical databases are cached in memory – little I/O – speed! Self Tuning, Healing, Autonomics Scale is becoming a commodity
Storage & Process - Process Automation
Serial process consists of:
One or more processing steps Communication & latency between steps
Optimization Opportunities
XML
Process
Analyze
MS Office, Map Point, Data Analyzer, Visio, Data Mining, etc.
The Number One Business Intelligence Engine in the World
Microsoft’s Business Intelligence Objective: Making BI Pervasive
© 2001 Microsoft Corporation. All rights reserved.
This presentation is for informational purposes only Microsoft makes no warranties, express or implied, in this summary.
Big Trend: “Born Digital” Data
100%
Percentage of Information “Born Digital”
Word Processing