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| No. | 单词 | 释义 |
|---|---|---|
| 401 | Marginal Distribution | 边缘分布 |
| 402 | Marginal Independence | 边缘独立性 |
| 403 | Marginalization | 边缘化 |
| 404 | Markov Chain | 马尔可夫链 |
| 405 | Markov Chain Monte Carlo | 马尔可夫链蒙特卡罗 |
| 406 | Markov Decision Process | 马尔可夫决策过程 |
| 407 | Markov Network | 马尔可夫网络 |
| 408 | Markov Process | 马尔可夫过程 |
| 409 | Markov Random Field | 马尔可夫随机场 |
| 410 | Mask | 掩码 |
| 411 | Matrix | 矩阵 |
| 412 | Matrix Inversion | 逆矩阵 |
| 413 | Max Pooling | 最大汇聚 |
| 414 | Maximal Clique | 最大团 |
| 415 | Maximum Entropy Model | 最大熵模型 |
| 416 | Maximum Likelihood Estimation | 极大似然估计 |
| 417 | Maximum Margin | 最大间隔 |
| 418 | Mean Filed | 平均场 |
| 419 | Mean Pooling | 平均汇聚 |
| 420 | Mean Squared Error | 均方误差 |
| 421 | Mean-Field | 平均场 |
| 422 | Memory Network | 记忆网络 |
| 423 | Message Passing | 消息传递 |
| 424 | Metric Learning | 度量学习 |
| 425 | Micro-R | 微查全率 |
| 426 | Minibatch | 小批量 |
| 427 | Minimal Description Length | 最小描述长度 |
| 428 | Minimax Game | 极小极大博弈 |
| 429 | Minkowski Distance | 闵可夫斯基距离 |
| 430 | Mixture of Experts | 混合专家模型 |
| 431 | Mixture-of-Gaussian | 高斯混合 |
| 432 | Model | 模型 |
| 433 | Model Selection | 模型选择 |
| 434 | Momentum Method | 动量法 |
| 435 | Monte Carlo Method | 蒙特卡罗方法 |
| 436 | Moral Graph | 端正图/道德图 |
| 437 | Moralization | 道德化 |
| 438 | Multi-Class Classification | 多分类 |
| 439 | Multi-Head Attention | 多头注意力 |
| 440 | Multi-Head Self-Attention | 多头自注意力 |
| 441 | Multi-Kernel Learning | 多核学习 |
| 442 | Multi-Label Learning | 多标记学习 |
| 443 | Multi-Layer Feedforward Neural Networks | 多层前馈神经网络 |
| 444 | Multi-Layer Perceptron | 多层感知机 |
| 445 | Multinomial Distribution | 多项分布 |
| 446 | Multiple Dimensional Scaling | 多维缩放 |
| 447 | Multiple Linear Regression | 多元线性回归 |
| 448 | Multitask Learning | 多任务学习 |
| 449 | Multivariate Normal Distribution | 多元正态分布 |
| 450 | Mutual Information | 互信息 |
| 451 | N-Gram Model | N元模型 |
| 452 | Naive Bayes | 朴素贝叶斯 |
| 453 | Naive Bayes Classifier | 朴素贝叶斯分类器 |
| 454 | Nearest Neighbor Classifier | 最近邻分类器 |
| 455 | Negative Log Likelihood | 负对数似然函数 |
| 456 | Neighbourhood Component Analysis | 近邻成分分析 |
| 457 | Net Input | 净输入 |
| 458 | Neural Network | 神经网络 |
| 459 | Neural Turing Machine | 神经图灵机 |
| 460 | Neuron | 神经元 |
| 461 | Newton Method | 牛顿法 |
| 462 | No Free Lunch Theorem | 没有免费午餐定理 |
| 463 | Noise-Contrastive Estimation | 噪声对比估计 |
| 464 | Nominal Attribute | 列名属性 |
| 465 | Non-Convex Optimization | 非凸优化 |
| 466 | Non-Metric Distance | 非度量距离 |
| 467 | Non-Negative Matrix Factorization | 非负矩阵分解 |
| 468 | Non-Ordinal Attribute | 无序属性 |
| 469 | Norm | 范数 |
| 470 | Normal Distribution | 正态分布 |
| 471 | Normalization | 规范化 |
| 472 | Nuclear Norm | 核范数 |
| 473 | Number of Epochs | 轮数 |
| 474 | Numerical Attribute | 数值属性 |
| 475 | Object Detection | 目标检测 |
| 476 | Oblique Decision Tree | 斜决策树 |
| 477 | Occam's Razor | 奥卡姆剃刀 |
| 478 | Odds | 几率 |
| 479 | Off-Policy | 异策略 |
| 480 | On-Policy | 同策略 |
| 481 | One-Dependent Estimator | 独依赖估计 |
| 482 | One-Hot | 独热 |
| 483 | One-Shot Learning | 单试学习 |
| 484 | Online Learning | 在线学习 |
| 485 | Optimizer | 优化器 |
| 486 | Ordinal Attribute | 有序属性 |
| 487 | Orthogonal | 正交 |
| 488 | Orthogonal Matrix | 正交矩阵 |
| 489 | Out-Of-Bag Estimate | 包外估计 |
| 490 | Outlier | 异常点 |
| 491 | Over-Parameterized | 过度参数化 |
| 492 | Overfitting | 过拟合 |
| 493 | Oversampling | 过采样 |
| 494 | Pac-Learnable | PAC可学习 |
| 495 | Padding | 填充 |
| 496 | Pairwise Markov Property | 成对马尔可夫性 |
| 497 | Parallel Distributed Processing | 分布式并行处理 |
| 498 | Parameter | 参数 |
| 499 | Parameter Estimation | 参数估计 |
| 500 | Parameter Space | 参数空间 |
| 501 | Parameter Tuning | 调参 |
| 502 | Parametric ReLU | 参数化修正线性单元/参数化整流线性单元 |
| 503 | Part-Of-Speech Tagging | 词性标注 |
| 504 | Partial Derivative | 偏导数 |
| 505 | Partially Observable Markov Decision Processes | 部分可观测马尔可夫决策过程 |
| 506 | Partition Function | 配分函数 |
| 507 | Perceptron | 感知机 |
| 508 | Performance Measure | 性能度量 |
| 509 | Perplexity | 困惑度 |
| 510 | Pointer Network | 指针网络 |
| 511 | Policy | 策略 |
| 512 | Policy Gradient | 策略梯度 |
| 513 | Policy Iteration | 策略迭代 |
| 514 | Polynomial Kernel Function | 多项式核函数 |
| 515 | Pooling | 汇聚 |
| 516 | Pooling Layer | 汇聚层 |
| 517 | Positive Definite Matrix | 正定矩阵 |
| 518 | Post-Pruning | 后剪枝 |
| 519 | Potential Function | 势函数 |
| 520 | Power Method | 幂法 |
| 521 | Pre-Training | 预训练 |
| 522 | Precision | 查准率/准确率 |
| 523 | Prepruning | 预剪枝 |
| 524 | Primal Problem | 主问题 |
| 525 | Primary Visual Cortex | 初级视觉皮层 |
| 526 | Principal Component Analysis | 主成分分析 |
| 527 | Prior | 先验 |
| 528 | Probabilistic Context-Free Grammar | 概率上下文无关文法 |
| 529 | Probabilistic Graphical Model | 概率图模型 |
| 530 | Probabilistic Model | 概率模型 |
| 531 | Probability Density Function | 概率密度函数 |
| 532 | Probability Distribution | 概率分布 |
| 533 | Probably Approximately Correct | 概率近似正确 |
| 534 | Proposal Distribution | 提议分布 |
| 535 | Prototype-Based Clustering | 原型聚类 |
| 536 | Proximal Gradient Descent | 近端梯度下降 |
| 537 | Pruning | 剪枝 |
| 538 | Quadratic Loss Function | 平方损失函数 |
| 539 | Quadratic Programming | 二次规划 |
| 540 | Quasi Newton Method | 拟牛顿法 |
| 541 | Radial Basis Function | 径向基函数 |
| 542 | Random Forest | 随机森林 |
| 543 | Random Sampling | 随机采样 |
| 544 | Random Search | 随机搜索 |
| 545 | Random Variable | 随机变量 |
| 546 | Random Walk | 随机游走 |
| 547 | Recall | 查全率/召回率 |
| 548 | Receptive Field | 感受野 |
| 549 | Reconstruction Error | 重构误差 |
| 550 | Rectified Linear Unit | 修正线性单元/整流线性单元 |
| 551 | Recurrent Neural Network | 循环神经网络 |
| 552 | Recursive Neural Network | 递归神经网络 |
| 553 | Regression | 回归 |
| 554 | Regularization | 正则化 |
| 555 | Regularizer | 正则化项 |
| 556 | Reinforcement Learning | 强化学习 |
| 557 | Relative Entropy | 相对熵 |
| 558 | Reparameterization | 再参数化/重参数化 |
| 559 | Representation | 表示 |
| 560 | Representation Learning | 表示学习 |
| 561 | Representer Theorem | 表示定理 |
| 562 | Reproducing Kernel Hilbert Space | 再生核希尔伯特空间 |
| 563 | Rescaling | 再缩放 |
| 564 | Reset Gate | 重置门 |
| 565 | Residual Connection | 残差连接 |
| 566 | Residual Network | 残差网络 |
| 567 | Restricted Boltzmann Machine | 受限玻尔兹曼机 |
| 568 | Reward | 奖励 |
| 569 | Ridge Regression | 岭回归 |
| 570 | Right Singular Vector | 右奇异向量 |
| 571 | Risk | 风险 |
| 572 | Robustness | 稳健性 |
| 573 | Root Node | 根结点 |
| 574 | Rule Learning | 规则学习 |
| 575 | Saddle Point | 鞍点 |
| 576 | Sample | 样本 |
| 577 | Sample Complexity | 样本复杂度 |
| 578 | Sample Space | 样本空间 |
| 579 | Scalar | 标量 |
| 580 | Selective Ensemble | 选择性集成 |
| 581 | Self Information | 自信息 |
| 582 | Self-Attention | 自注意力 |
| 583 | Self-Organizing Map | 自组织映射网 |
| 584 | Self-Training | 自训练 |
| 585 | Semi-Definite Programming | 半正定规划 |
| 586 | Semi-Naive Bayes Classifiers | 半朴素贝叶斯分类器 |
| 587 | Semi-Restricted Boltzmann Machine | 半受限玻尔兹曼机 |
| 588 | Semi-Supervised Clustering | 半监督聚类 |
| 589 | Semi-Supervised Learning | 半监督学习 |
| 590 | Semi-Supervised Support Vector Machine | 半监督支持向量机 |
| 591 | Sentiment Analysis | 情感分析 |
| 592 | Separating Hyperplane | 分离超平面 |
| 593 | Sequential Covering | 序贯覆盖 |
| 594 | Sigmoid Belief Network | Sigmoid信念网络 |
| 595 | Sigmoid Function | Sigmoid函数 |
| 596 | Signed Distance | 带符号距离 |
| 597 | Similarity Measure | 相似度度量 |
| 598 | Simulated Annealing | 模拟退火 |
| 599 | Simultaneous Localization And Mapping | 即时定位与地图构建 |
| 600 | Singular Value | 奇异值 |