机器学习专业英语单词

常用英语词汇-andrew Ng课程

• [1 ] intensity 强度
• [2 ] Regression 回归
• [3 ] Loss function 损失函数
• [4 ] non-convex 非凸函数
• [5 ] neural network 神经网络
• [ ] supervised learning 监督学习
• [ ] regression problem 回归问题处理的是连续的问题
• [ ] classification problem 分类问题处理的问题是离散的而不是连续的
  回归问题和分类问题的区别应该在于 回归问题的结果是连续的，分类问题的结果是离散的。
• [ ] discreet value 离散值
• [ ] support vector machines 支持向量机,用来处理分类算法中输入的维度不单一的情况（甚至输入维度为无穷）
• [ ] learning theory 学习理论
• [ ] learning algorithms 学习算法
• [ ] unsupervised learning 无监督学习
• [ ] gradient descent 梯度下降
• [ ] linear regression 线性回归
• [ ] Neural Network 神经网络
• [ ] gradient descent 梯度下降 监督学习的一种算法，用来拟合的算法
• [ ] normal equations
• [ ] linear algebra 线性代数 原谅我英语不太好
• [ ] superscript上标
• [ ] exponentiation 指数
• [ ] training set 训练集合
• [ ] training example 训练样本
• [ ] hypothesis 假设，用来表示学习算法的输出，叫我们不要太纠结H的意思，因为这只是历史的惯例
• [ ] LMS algorithm “least mean squares” 最小二乘法算法
• [ ] batch gradient descent 批量梯度下降，因为每次都会计算 最小拟合的方差，所以运算慢
• [ ] constantly gradient descent 字幕组翻译成“随机梯度下降” 我怎么觉得是“常量梯度下降”也就是梯度下降的运算次数不变，一般比批量梯度下降速度快，但是通常不是那么准确
• [ ] iterative algorithm 迭代算法
• [ ] partial derivative 偏导数
• [ ] contour 等高线
• [ ] quadratic function 二元函数
• [ ] locally weighted regression局部加权回归
• [ ] underfitting欠拟合
• [ ] overfitting 过拟合
• [ ] non-parametric learning algorithms 无参数学习算法
• [ ] parametric learning algorithm 参数学习算法

• [ ] other

• [ ] activation 激活值

• [ ] activation function 激活函数
• [ ] additive noise 加性噪声
• [ ] autoencoder 自编码器
• [ ] Autoencoders 自编码算法
• [ ] average firing rate 平均激活率
• [ ] average sum-of-squares error 均方差
• [ ] backpropagation 后向传播
• [ ] basis 基
• [ ] basis feature vectors 特征基向量
• [50 ] batch gradient ascent 批量梯度上升法
• [ ] Bayesian regularization method 贝叶斯规则化方法
• [ ] Bernoulli random variable 伯努利随机变量
• [ ] bias term 偏置项
• [ ] binary classfication 二元分类
• [ ] class labels 类型标记
• [ ] concatenation 级联
• [ ] conjugate gradient 共轭梯度
• [ ] contiguous groups 联通区域
• [ ] convex optimization software 凸优化软件
• [ ] convolution 卷积
• [ ] cost function 代价函数
• [ ] covariance matrix 协方差矩阵
• [ ] DC component 直流分量
• [ ] decorrelation 去相关
• [ ] degeneracy 退化
• [ ] demensionality reduction 降维
• [ ] derivative 导函数
• [ ] diagonal 对角线
• [ ] diffusion of gradients 梯度的弥散
• [ ] eigenvalue 特征值
• [ ] eigenvector 特征向量
• [ ] error term 残差
• [ ] feature matrix 特征矩阵
• [ ] feature standardization 特征标准化
• [ ] feedforward architectures 前馈结构算法
• [ ] feedforward neural network 前馈神经网络
• [ ] feedforward pass 前馈传导
• [ ] fine-tuned 微调
• [ ] first-order feature 一阶特征
• [ ] forward pass 前向传导
• [ ] forward propagation 前向传播
• [ ] Gaussian prior 高斯先验概率
• [ ] generative model 生成模型
• [ ] gradient descent 梯度下降
• [ ] Greedy layer-wise training 逐层贪婪训练方法
• [ ] grouping matrix 分组矩阵
• [ ] Hadamard product 阿达马乘积
• [ ] Hessian matrix Hessian 矩阵
• [ ] hidden layer 隐含层
• [ ] hidden units 隐藏神经元
• [ ] Hierarchical grouping 层次型分组
• [ ] higher-order features 更高阶特征
• [ ] highly non-convex optimization problem 高度非凸的优化问题
• [ ] histogram 直方图
• [ ] hyperbolic tangent 双曲正切函数
• [ ] hypothesis 估值，假设
• [ ] identity activation function 恒等激励函数
• [ ] IID 独立同分布
• [ ] illumination 照明
• [100 ] inactive 抑制
• [ ] independent component analysis 独立成份分析
• [ ] input domains 输入域
• [ ] input layer 输入层
• [ ] intensity 亮度/灰度
• [ ] intercept term 截距
• [ ] KL divergence 相对熵
• [ ] KL divergence KL分散度
• [ ] k-Means K-均值
• [ ] learning rate 学习速率
• [ ] least squares 最小二乘法
• [ ] linear correspondence 线性响应
• [ ] linear superposition 线性叠加
• [ ] line-search algorithm 线搜索算法
• [ ] local mean subtraction 局部均值消减
• [ ] local optima 局部最优解
• [ ] logistic regression 逻辑回归
• [ ] loss function 损失函数
• [ ] low-pass filtering 低通滤波
• [ ] magnitude 幅值
• [ ] MAP 极大后验估计
• [ ] maximum likelihood estimation 极大似然估计
• [ ] mean 平均值
• [ ] MFCC Mel 倒频系数
• [ ] multi-class classification 多元分类
• [ ] neural networks 神经网络
• [ ] neuron 神经元
• [ ] Newton’s method 牛顿法
• [ ] non-convex function 非凸函数
• [ ] non-linear feature 非线性特征
• [ ] norm 范式
• [ ] norm bounded 有界范数
• [ ] norm constrained 范数约束
• [ ] normalization 归一化
• [ ] numerical roundoff errors 数值舍入误差
• [ ] numerically checking 数值检验
• [ ] numerically reliable 数值计算上稳定
• [ ] object detection 物体检测
• [ ] objective function 目标函数
• [ ] off-by-one error 缺位错误
• [ ] orthogonalization 正交化
• [ ] output layer 输出层
• [ ] overall cost function 总体代价函数
• [ ] over-complete basis 超完备基
• [ ] over-fitting 过拟合
• [ ] parts of objects 目标的部件
• [ ] part-whole decompostion 部分-整体分解
• [ ] PCA 主元分析
• [ ] penalty term 惩罚因子
• [ ] per-example mean subtraction 逐样本均值消减
• [150 ] pooling 池化
• [ ] pretrain 预训练
• [ ] principal components analysis 主成份分析
• [ ] quadratic constraints 二次约束
• [ ] RBMs 受限Boltzman机
• [ ] reconstruction based models 基于重构的模型
• [ ] reconstruction cost 重建代价
• [ ] reconstruction term 重构项
• [ ] redundant 冗余
• [ ] reflection matrix 反射矩阵
• [ ] regularization 正则化
• [ ] regularization term 正则化项
• [ ] rescaling 缩放
• [ ] robust 鲁棒性
• [ ] run 行程
• [ ] second-order feature 二阶特征
• [ ] sigmoid activation function S型激励函数
• [ ] significant digits 有效数字
• [ ] singular value 奇异值
• [ ] singular vector 奇异向量
• [ ] smoothed L1 penalty 平滑的L1范数惩罚
• [ ] Smoothed topographic L1 sparsity penalty 平滑地形L1稀疏惩罚函数
• [ ] smoothing 平滑
• [ ] Softmax Regresson Softmax回归
• [ ] sorted in decreasing order 降序排列
• [ ] source features 源特征
• [ ] sparse autoencoder 消减归一化
• [ ] Sparsity 稀疏性
• [ ] sparsity parameter 稀疏性参数
• [ ] sparsity penalty 稀疏惩罚
• [ ] square function 平方函数
• [ ] squared-error 方差
• [ ] stationary 平稳性（不变性）
• [ ] stationary stochastic process 平稳随机过程
• [ ] step-size 步长值
• [ ] supervised learning 监督学习
• [ ] symmetric positive semi-definite matrix 对称半正定矩阵
• [ ] symmetry breaking 对称失效
• [ ] tanh function 双曲正切函数
• [ ] the average activation 平均活跃度
• [ ] the derivative checking method 梯度验证方法
• [ ] the empirical distribution 经验分布函数
• [ ] the energy function 能量函数
• [ ] the Lagrange dual 拉格朗日对偶函数
• [ ] the log likelihood 对数似然函数
• [ ] the pixel intensity value 像素灰度值
• [ ] the rate of convergence 收敛速度
• [ ] topographic cost term 拓扑代价项
• [ ] topographic ordered 拓扑秩序
• [ ] transformation 变换
• [200 ] translation invariant 平移不变性
• [ ] trivial answer 平凡解
• [ ] under-complete basis 不完备基
• [ ] unrolling 组合扩展
• [ ] unsupervised learning 无监督学习
• [ ] variance 方差
• [ ] vecotrized implementation 向量化实现
• [ ] vectorization 矢量化
• [ ] visual cortex 视觉皮层
• [ ] weight decay 权重衰减
• [ ] weighted average 加权平均值
• [ ] whitening 白化

• [ ] zero-mean 均值为零

• [ ] Letter A

• [ ] Accumulated error backpropagation 累积误差逆传播

• [ ] Activation Function 激活函数
• [ ] Adaptive Resonance Theory/ART 自适应谐振理论
• [ ] Addictive model 加性学习
• [ ] Adversarial Networks 对抗网络
• [ ] Affine Layer 仿射层
• [ ] Affinity matrix 亲和矩阵
• [ ] Agent 代理 / 智能体
• [ ] Algorithm 算法
• [ ] Alpha-beta pruning α-β剪枝
• [ ] Anomaly detection 异常检测
• [ ] Approximation 近似
• [ ] Area Under ROC Curve／AUC Roc 曲线下面积
• [ ] Artificial General Intelligence/AGI 通用人工智能
• [ ] Artificial Intelligence/AI 人工智能
• [ ] Association analysis 关联分析
• [ ] Attention mechanism 注意力机制
• [ ] Attribute conditional independence assumption 属性条件独立性假设
• [ ] Attribute space 属性空间
• [ ] Attribute value 属性值
• [ ] Autoencoder 自编码器
• [ ] Automatic speech recognition 自动语音识别
• [ ] Automatic summarization 自动摘要
• [ ] Average gradient 平均梯度

• [ ] Average-Pooling 平均池化

• [ ] Letter B

• [ ] Backpropagation Through Time 通过时间的反向传播

• [ ] Backpropagation/BP 反向传播
• [ ] Base learner 基学习器
• [ ] Base learning algorithm 基学习算法
• [ ] Batch Normalization/BN 批量归一化
• [ ] Bayes decision rule 贝叶斯判定准则
• [250 ] Bayes Model Averaging／BMA 贝叶斯模型平均
• [ ] Bayes optimal classifier 贝叶斯最优分类器
• [ ] Bayesian decision theory 贝叶斯决策论
• [ ] Bayesian network 贝叶斯网络
• [ ] Between-class scatter matrix 类间散度矩阵
• [ ] Bias 偏置 / 偏差
• [ ] Bias-variance decomposition 偏差-方差分解
• [ ] Bias-Variance Dilemma 偏差 – 方差困境
• [ ] Bi-directional Long-Short Term Memory/Bi-LSTM 双向长短期记忆
• [ ] Binary classification 二分类
• [ ] Binomial test 二项检验
• [ ] Bi-partition 二分法
• [ ] Boltzmann machine 玻尔兹曼机
• [ ] Bootstrap sampling 自助采样法／可重复采样／有放回采样
• [ ] Bootstrapping 自助法

• [ ] Break-Event Point／BEP 平衡点

• [ ] Letter C

• [ ] Calibration 校准

• [ ] Cascade-Correlation 级联相关
• [ ] Categorical attribute 离散属性
• [ ] Class-conditional probability 类条件概率
• [ ] Classification and regression tree/CART 分类与回归树
• [ ] Classifier 分类器
• [ ] Class-imbalance 类别不平衡
• [ ] Closed -form 闭式
• [ ] Cluster 簇/类/集群
• [ ] Cluster analysis 聚类分析
• [ ] Clustering 聚类
• [ ] Clustering ensemble 聚类集成
• [ ] Co-adapting 共适应
• [ ] Coding matrix 编码矩阵
• [ ] COLT 国际学习理论会议
• [ ] Committee-based learning 基于委员会的学习
• [ ] Competitive learning 竞争型学习
• [ ] Component learner 组件学习器
• [ ] Comprehensibility 可解释性
• [ ] Computation Cost 计算成本
• [ ] Computational Linguistics 计算语言学
• [ ] Computer vision 计算机视觉
• [ ] Concept drift 概念漂移
• [ ] Concept Learning System /CLS 概念学习系统
• [ ] Conditional entropy 条件熵
• [ ] Conditional mutual information 条件互信息
• [ ] Conditional Probability Table／CPT 条件概率表
• [ ] Conditional random field/CRF 条件随机场
• [ ] Conditional risk 条件风险
• [ ] Confidence 置信度
• [ ] Confusion matrix 混淆矩阵
• [300 ] Connection weight 连接权
• [ ] Connectionism 连结主义
• [ ] Consistency 一致性／相合性
• [ ] Contingency table 列联表
• [ ] Continuous attribute 连续属性
• [ ] Convergence 收敛
• [ ] Conversational agent 会话智能体
• [ ] Convex quadratic programming 凸二次规划
• [ ] Convexity 凸性
• [ ] Convolutional neural network/CNN 卷积神经网络
• [ ] Co-occurrence 同现
• [ ] Correlation coefficient 相关系数
• [ ] Cosine similarity 余弦相似度
• [ ] Cost curve 成本曲线
• [ ] Cost Function 成本函数
• [ ] Cost matrix 成本矩阵
• [ ] Cost-sensitive 成本敏感
• [ ] Cross entropy 交叉熵
• [ ] Cross validation 交叉验证
• [ ] Crowdsourcing 众包
• [ ] Curse of dimensionality 维数灾难
• [ ] Cut point 截断点

• [ ] Cutting plane algorithm 割平面法

• [ ] Letter D

• [ ] Data mining 数据挖掘

• [ ] Data set 数据集
• [ ] Decision Boundary 决策边界
• [ ] Decision stump 决策树桩
• [ ] Decision tree 决策树／判定树
• [ ] Deduction 演绎
• [ ] Deep Belief Network 深度信念网络
• [ ] Deep Convolutional Generative Adversarial Network/DCGAN 深度卷积生成对抗网络
• [ ] Deep learning 深度学习
• [ ] Deep neural network/DNN 深度神经网络
• [ ] Deep Q-Learning 深度 Q 学习
• [ ] Deep Q-Network 深度 Q 网络
• [ ] Density estimation 密度估计
• [ ] Density-based clustering 密度聚类
• [ ] Differentiable neural computer 可微分神经计算机
• [ ] Dimensionality reduction algorithm 降维算法
• [ ] Directed edge 有向边
• [ ] Disagreement measure 不合度量
• [ ] Discriminative model 判别模型
• [ ] Discriminator 判别器
• [ ] Distance measure 距离度量
• [ ] Distance metric learning 距离度量学习
• [ ] Distribution 分布
• [ ] Divergence 散度
• [350 ] Diversity measure 多样性度量／差异性度量
• [ ] Domain adaption 领域自适应
• [ ] Downsampling 下采样
• [ ] D-separation （Directed separation） 有向分离
• [ ] Dual problem 对偶问题
• [ ] Dummy node 哑结点
• [ ] Dynamic Fusion 动态融合

• [ ] Dynamic programming 动态规划

• [ ] Letter E

• [ ] Eigenvalue decomposition 特征值分解

• [ ] Embedding 嵌入
• [ ] Emotional analysis 情绪分析
• [ ] Empirical conditional entropy 经验条件熵
• [ ] Empirical entropy 经验熵
• [ ] Empirical error 经验误差
• [ ] Empirical risk 经验风险
• [ ] End-to-End 端到端
• [ ] Energy-based model 基于能量的模型
• [ ] Ensemble learning 集成学习
• [ ] Ensemble pruning 集成修剪
• [ ] Error Correcting Output Codes／ECOC 纠错输出码
• [ ] Error rate 错误率
• [ ] Error-ambiguity decomposition 误差-分歧分解
• [ ] Euclidean distance 欧氏距离
• [ ] Evolutionary computation 演化计算
• [ ] Expectation-Maximization 期望最大化
• [ ] Expected loss 期望损失
• [ ] Exploding Gradient Problem 梯度爆炸问题
• [ ] Exponential loss function 指数损失函数

• [ ] Extreme Learning Machine/ELM 超限学习机

• [ ] Letter F

• [ ] Factorization 因子分解

• [ ] False negative 假负类
• [ ] False positive 假正类
• [ ] False Positive Rate/FPR 假正例率
• [ ] Feature engineering 特征工程
• [ ] Feature selection 特征选择
• [ ] Feature vector 特征向量
• [ ] Featured Learning 特征学习
• [ ] Feedforward Neural Networks/FNN 前馈神经网络
• [ ] Fine-tuning 微调
• [ ] Flipping output 翻转法
• [ ] Fluctuation 震荡
• [ ] Forward stagewise algorithm 前向分步算法
• [ ] Frequentist 频率主义学派
• [ ] Full-rank matrix 满秩矩阵

• [400 ] Functional neuron 功能神经元

• [ ] Letter G

• [ ] Gain ratio 增益率

• [ ] Game theory 博弈论
• [ ] Gaussian kernel function 高斯核函数
• [ ] Gaussian Mixture Model 高斯混合模型
• [ ] General Problem Solving 通用问题求解
• [ ] Generalization 泛化
• [ ] Generalization error 泛化误差
• [ ] Generalization error bound 泛化误差上界
• [ ] Generalized Lagrange function 广义拉格朗日函数
• [ ] Generalized linear model 广义线性模型
• [ ] Generalized Rayleigh quotient 广义瑞利商
• [ ] Generative Adversarial Networks/GAN 生成对抗网络
• [ ] Generative Model 生成模型
• [ ] Generator 生成器
• [ ] Genetic Algorithm/GA 遗传算法
• [ ] Gibbs sampling 吉布斯采样
• [ ] Gini index 基尼指数
• [ ] Global minimum 全局最小
• [ ] Global Optimization 全局优化
• [ ] Gradient boosting 梯度提升
• [ ] Gradient Descent 梯度下降
• [ ] Graph theory 图论

• [ ] Ground-truth 真相／真实

• [ ] Letter H

• [ ] Hard margin 硬间隔

• [ ] Hard voting 硬投票
• [ ] Harmonic mean 调和平均
• [ ] Hesse matrix 海塞矩阵
• [ ] Hidden dynamic model 隐动态模型
• [ ] Hidden layer 隐藏层
• [ ] Hidden Markov Model/HMM 隐马尔可夫模型
• [ ] Hierarchical clustering 层次聚类
• [ ] Hilbert space 希尔伯特空间
• [ ] Hinge loss function 合页损失函数
• [ ] Hold-out 留出法
• [ ] Homogeneous 同质
• [ ] Hybrid computing 混合计算
• [ ] Hyperparameter 超参数
• [ ] Hypothesis 假设

• [ ] Hypothesis test 假设验证

• [ ] Letter I

• [ ] ICML 国际机器学习会议

• [450 ] Improved iterative scaling/IIS 改进的迭代尺度法
• [ ] Incremental learning 增量学习
• [ ] Independent and identically distributed/i.i.d. 独立同分布
• [ ] Independent Component Analysis/ICA 独立成分分析
• [ ] Indicator function 指示函数
• [ ] Individual learner 个体学习器
• [ ] Induction 归纳
• [ ] Inductive bias 归纳偏好
• [ ] Inductive learning 归纳学习
• [ ] Inductive Logic Programming／ILP 归纳逻辑程序设计
• [ ] Information entropy 信息熵
• [ ] Information gain 信息增益
• [ ] Input layer 输入层
• [ ] Insensitive loss 不敏感损失
• [ ] Inter-cluster similarity 簇间相似度
• [ ] International Conference for Machine Learning/ICML 国际机器学习大会
• [ ] Intra-cluster similarity 簇内相似度
• [ ] Intrinsic value 固有值
• [ ] Isometric Mapping/Isomap 等度量映射
• [ ] Isotonic regression 等分回归

• [ ] Iterative Dichotomiser 迭代二分器

• [ ] Letter K

• [ ] Kernel method 核方法

• [ ] Kernel trick 核技巧
• [ ] Kernelized Linear Discriminant Analysis／KLDA 核线性判别分析
• [ ] K-fold cross validation k 折交叉验证／k 倍交叉验证
• [ ] K-Means Clustering K – 均值聚类
• [ ] K-Nearest Neighbours Algorithm/KNN K近邻算法
• [ ] Knowledge base 知识库

• [ ] Knowledge Representation 知识表征

• [ ] Letter L

• [ ] Label space 标记空间

• [ ] Lagrange duality 拉格朗日对偶性
• [ ] Lagrange multiplier 拉格朗日乘子
• [ ] Laplace smoothing 拉普拉斯平滑
• [ ] Laplacian correction 拉普拉斯修正
• [ ] Latent Dirichlet Allocation 隐狄利克雷分布
• [ ] Latent semantic analysis 潜在语义分析
• [ ] Latent variable 隐变量
• [ ] Lazy learning 懒惰学习
• [ ] Learner 学习器
• [ ] Learning by analogy 类比学习
• [ ] Learning rate 学习率
• [ ] Learning Vector Quantization/LVQ 学习向量量化
• [ ] Least squares regression tree 最小二乘回归树
• [ ] Leave-One-Out/LOO 留一法
• [500 ] linear chain conditional random field 线性链条件随机场
• [ ] Linear Discriminant Analysis／LDA 线性判别分析
• [ ] Linear model 线性模型
• [ ] Linear Regression 线性回归
• [ ] Link function 联系函数
• [ ] Local Markov property 局部马尔可夫性
• [ ] Local minimum 局部最小
• [ ] Log likelihood 对数似然
• [ ] Log odds／logit 对数几率
• [ ] Logistic Regression Logistic 回归
• [ ] Log-likelihood 对数似然
• [ ] Log-linear regression 对数线性回归
• [ ] Long-Short Term Memory/LSTM 长短期记忆

• [ ] Loss function 损失函数

• [ ] Letter M

• [ ] Machine translation/MT 机器翻译

• [ ] Macron-P 宏查准率
• [ ] Macron-R 宏查全率
• [ ] Majority voting 绝对多数投票法
• [ ] Manifold assumption 流形假设
• [ ] Manifold learning 流形学习
• [ ] Margin theory 间隔理论
• [ ] Marginal distribution 边际分布
• [ ] Marginal independence 边际独立性
• [ ] Marginalization 边际化
• [ ] Markov Chain Monte Carlo/MCMC 马尔可夫链蒙特卡罗方法
• [ ] Markov Random Field 马尔可夫随机场
• [ ] Maximal clique 最大团
• [ ] Maximum Likelihood Estimation/MLE 极大似然估计／极大似然法
• [ ] Maximum margin 最大间隔
• [ ] Maximum weighted spanning tree 最大带权生成树
• [ ] Max-Pooling 最大池化
• [ ] Mean squared error 均方误差
• [ ] Meta-learner 元学习器
• [ ] Metric learning 度量学习
• [ ] Micro-P 微查准率
• [ ] Micro-R 微查全率
• [ ] Minimal Description Length/MDL 最小描述长度
• [ ] Minimax game 极小极大博弈
• [ ] Misclassification cost 误分类成本
• [ ] Mixture of experts 混合专家
• [ ] Momentum 动量
• [ ] Moral graph 道德图／端正图
• [ ] Multi-class classification 多分类
• [ ] Multi-document summarization 多文档摘要
• [ ] Multi-layer feedforward neural networks 多层前馈神经网络
• [ ] Multilayer Perceptron/MLP 多层感知器
• [ ] Multimodal learning 多模态学习
• [550 ] Multiple Dimensional Scaling 多维缩放
• [ ] Multiple linear regression 多元线性回归
• [ ] Multi-response Linear Regression ／MLR 多响应线性回归

• [ ] Mutual information 互信息

• [ ] Letter N

• [ ] Naive bayes 朴素贝叶斯

• [ ] Naive Bayes Classifier 朴素贝叶斯分类器
• [ ] Named entity recognition 命名实体识别
• [ ] Nash equilibrium 纳什均衡
• [ ] Natural language generation/NLG 自然语言生成
• [ ] Natural language processing 自然语言处理
• [ ] Negative class 负类
• [ ] Negative correlation 负相关法
• [ ] Negative Log Likelihood 负对数似然
• [ ] Neighbourhood Component Analysis/NCA 近邻成分分析
• [ ] Neural Machine Translation 神经机器翻译
• [ ] Neural Turing Machine 神经图灵机
• [ ] Newton method 牛顿法
• [ ] NIPS 国际神经信息处理系统会议
• [ ] No Free Lunch Theorem／NFL 没有免费的午餐定理
• [ ] Noise-contrastive estimation 噪音对比估计
• [ ] Nominal attribute 列名属性
• [ ] Non-convex optimization 非凸优化
• [ ] Nonlinear model 非线性模型
• [ ] Non-metric distance 非度量距离
• [ ] Non-negative matrix factorization 非负矩阵分解
• [ ] Non-ordinal attribute 无序属性
• [ ] Non-Saturating Game 非饱和博弈
• [ ] Norm 范数
• [ ] Normalization 归一化
• [ ] Nuclear norm 核范数

• [ ] Numerical attribute 数值属性

• [ ] Letter O

• [ ] Objective function 目标函数

• [ ] Oblique decision tree 斜决策树
• [ ] Occam’s razor 奥卡姆剃刀
• [ ] Odds 几率
• [ ] Off-Policy 离策略
• [ ] One shot learning 一次性学习
• [ ] One-Dependent Estimator／ODE 独依赖估计
• [ ] On-Policy 在策略
• [ ] Ordinal attribute 有序属性
• [ ] Out-of-bag estimate 包外估计
• [ ] Output layer 输出层
• [ ] Output smearing 输出调制法
• [ ] Overfitting 过拟合／过配

• [600 ] Oversampling 过采样

• [ ] Letter P

• [ ] Paired t-test 成对 t 检验

• [ ] Pairwise 成对型
• [ ] Pairwise Markov property 成对马尔可夫性
• [ ] Parameter 参数
• [ ] Parameter estimation 参数估计
• [ ] Parameter tuning 调参
• [ ] Parse tree 解析树
• [ ] Particle Swarm Optimization/PSO 粒子群优化算法
• [ ] Part-of-speech tagging 词性标注
• [ ] Perceptron 感知机
• [ ] Performance measure 性能度量
• [ ] Plug and Play Generative Network 即插即用生成网络
• [ ] Plurality voting 相对多数投票法
• [ ] Polarity detection 极性检测
• [ ] Polynomial kernel function 多项式核函数
• [ ] Pooling 池化
• [ ] Positive class 正类
• [ ] Positive definite matrix 正定矩阵
• [ ] Post-hoc test 后续检验
• [ ] Post-pruning 后剪枝
• [ ] potential function 势函数
• [ ] Precision 查准率／准确率
• [ ] Prepruning 预剪枝
• [ ] Principal component analysis/PCA 主成分分析
• [ ] Principle of multiple explanations 多释原则
• [ ] Prior 先验
• [ ] Probability Graphical Model 概率图模型
• [ ] Proximal Gradient Descent/PGD 近端梯度下降
• [ ] Pruning 剪枝

• [ ] Pseudo-label 伪标记

• [ ] Letter Q

• [ ] Quantized Neural Network 量子化神经网络

• [ ] Quantum computer 量子计算机
• [ ] Quantum Computing 量子计算

• [ ] Quasi Newton method 拟牛顿法

• [ ] Letter R

• [ ] Radial Basis Function／RBF 径向基函数

• [ ] Random Forest Algorithm 随机森林算法
• [ ] Random walk 随机漫步
• [ ] Recall 查全率／召回率
• [ ] Receiver Operating Characteristic/ROC 受试者工作特征
• [ ] Rectified Linear Unit/ReLU 线性修正单元
• [650 ] Recurrent Neural Network 循环神经网络
• [ ] Recursive neural network 递归神经网络
• [ ] Reference model 参考模型
• [ ] Regression 回归
• [ ] Regularization 正则化
• [ ] Reinforcement learning/RL 强化学习
• [ ] Representation learning 表征学习
• [ ] Representer theorem 表示定理
• [ ] reproducing kernel Hilbert space/RKHS 再生核希尔伯特空间
• [ ] Re-sampling 重采样法
• [ ] Rescaling 再缩放
• [ ] Residual Mapping 残差映射
• [ ] Residual Network 残差网络
• [ ] Restricted Boltzmann Machine/RBM 受限玻尔兹曼机
• [ ] Restricted Isometry Property/RIP 限定等距性
• [ ] Re-weighting 重赋权法
• [ ] Robustness 稳健性/鲁棒性
• [ ] Root node 根结点
• [ ] Rule Engine 规则引擎

• [ ] Rule learning 规则学习

• [ ] Letter S

• [ ] Saddle point 鞍点

• [ ] Sample space 样本空间
• [ ] Sampling 采样
• [ ] Score function 评分函数
• [ ] Self-Driving 自动驾驶
• [ ] Self-Organizing Map／SOM 自组织映射
• [ ] Semi-naive Bayes classifiers 半朴素贝叶斯分类器
• [ ] Semi-Supervised Learning 半监督学习
• [ ] semi-Supervised Support Vector Machine 半监督支持向量机
• [ ] Sentiment analysis 情感分析
• [ ] Separating hyperplane 分离超平面
• [ ] Sigmoid function Sigmoid 函数
• [ ] Similarity measure 相似度度量
• [ ] Simulated annealing 模拟退火
• [ ] Simultaneous localization and mapping 同步定位与地图构建
• [ ] Singular Value Decomposition 奇异值分解
• [ ] Slack variables 松弛变量
• [ ] Smoothing 平滑
• [ ] Soft margin 软间隔
• [ ] Soft margin maximization 软间隔最大化
• [ ] Soft voting 软投票
• [ ] Sparse representation 稀疏表征
• [ ] Sparsity 稀疏性
• [ ] Specialization 特化
• [ ] Spectral Clustering 谱聚类
• [ ] Speech Recognition 语音识别
• [ ] Splitting variable 切分变量
• [700 ] Squashing function 挤压函数
• [ ] Stability-plasticity dilemma 可塑性-稳定性困境
• [ ] Statistical learning 统计学习
• [ ] Status feature function 状态特征函
• [ ] Stochastic gradient descent 随机梯度下降
• [ ] Stratified sampling 分层采样
• [ ] Structural risk 结构风险
• [ ] Structural risk minimization/SRM 结构风险最小化
• [ ] Subspace 子空间
• [ ] Supervised learning 监督学习／有导师学习
• [ ] support vector expansion 支持向量展式
• [ ] Support Vector Machine/SVM 支持向量机
• [ ] Surrogat loss 替代损失
• [ ] Surrogate function 替代函数
• [ ] Symbolic learning 符号学习
• [ ] Symbolism 符号主义

• [ ] Synset 同义词集

• [ ] Letter T

• [ ] T-Distribution Stochastic Neighbour Embedding/t-SNE T – 分布随机近邻嵌入

• [ ] Tensor 张量
• [ ] Tensor Processing Units/TPU 张量处理单元
• [ ] The least square method 最小二乘法
• [ ] Threshold 阈值
• [ ] Threshold logic unit 阈值逻辑单元
• [ ] Threshold-moving 阈值移动
• [ ] Time Step 时间步骤
• [ ] Tokenization 标记化
• [ ] Training error 训练误差
• [ ] Training instance 训练示例／训练例
• [ ] Transductive learning 直推学习
• [ ] Transfer learning 迁移学习
• [ ] Treebank 树库
• [ ] Tria-by-error 试错法
• [ ] True negative 真负类
• [ ] True positive 真正类
• [ ] True Positive Rate/TPR 真正例率
• [ ] Turing Machine 图灵机

• [ ] Twice-learning 二次学习

• [ ] Letter U

• [ ] Underfitting 欠拟合／欠配

• [ ] Undersampling 欠采样
• [ ] Understandability 可理解性
• [ ] Unequal cost 非均等代价
• [ ] Unit-step function 单位阶跃函数
• [ ] Univariate decision tree 单变量决策树
• [ ] Unsupervised learning 无监督学习／无导师学习
• [ ] Unsupervised layer-wise training 无监督逐层训练

• [ ] Upsampling 上采样

• [ ] Letter V

• [ ] Vanishing Gradient Problem 梯度消失问题

• [ ] Variational inference 变分推断
• [ ] VC Theory VC维理论
• [ ] Version space 版本空间
• [ ] Viterbi algorithm 维特比算法

• [760 ] Von Neumann architecture 冯 · 诺伊曼架构

• [ ] Letter W

• [ ] Wasserstein GAN/WGAN Wasserstein生成对抗网络

• [ ] Weak learner 弱学习器
• [ ] Weight 权重
• [ ] Weight sharing 权共享
• [ ] Weighted voting 加权投票法
• [ ] Within-class scatter matrix 类内散度矩阵
• [ ] Word embedding 词嵌入

• [ ] Word sense disambiguation 词义消歧

• [ ] Letter Z

• [ ] Zero-data learning 零数据学习

• [ ] Zero-shot learning 零次学习

• [ ] A

• [ ] approximations近似值

• [ ] arbitrary随意的
• [ ] affine仿射的
• [ ] arbitrary任意的
• [ ] amino acid氨基酸
• [ ] amenable经得起检验的
• [ ] axiom公理，原则
• [ ] abstract提取
• [ ] architecture架构，体系结构；建造业
• [ ] absolute绝对的
• [ ] arsenal军火库
• [ ] assignment分配
• [ ] algebra线性代数
• [ ] asymptotically无症状的

• [ ] appropriate恰当的

• [ ] B

• [ ] bias偏差

• [ ] brevity简短，简洁；短暂
• [800 ] broader广泛
• [ ] briefly简短的

• [ ] batch批量

• [ ] C

• [ ] convergence 收敛，集中到一点

• [ ] convex凸的
• [ ] contours轮廓
• [ ] constraint约束
• [ ] constant常理
• [ ] commercial商务的
• [ ] complementarity补充
• [ ] coordinate ascent同等级上升
• [ ] clipping剪下物；剪报；修剪
• [ ] component分量；部件
• [ ] continuous连续的
• [ ] covariance协方差
• [ ] canonical正规的，正则的
• [ ] concave非凸的
• [ ] corresponds相符合；相当；通信
• [ ] corollary推论
• [ ] concrete具体的事物，实在的东西
• [ ] cross validation交叉验证
• [ ] correlation相互关系
• [ ] convention约定
• [ ] cluster一簇
• [ ] centroids 质心，形心
• [ ] converge收敛
• [ ] computationally计算(机)的

• [ ] calculus计算

• [ ] D

• [ ] derive获得，取得

• [ ] dual二元的
• [ ] duality二元性；二象性；对偶性
• [ ] derivation求导；得到；起源
• [ ] denote预示，表示，是…的标志；意味着，[逻]指称
• [ ] divergence 散度；发散性
• [ ] dimension尺度，规格；维数
• [ ] dot小圆点
• [ ] distortion变形
• [ ] density概率密度函数
• [ ] discrete离散的
• [ ] discriminative有识别能力的
• [ ] diagonal对角
• [ ] dispersion分散，散开
• [ ] determinant决定因素

• [849 ] disjoint不相交的

• [ ] E

• [ ] encounter遇到

• [ ] ellipses椭圆
• [ ] equality等式
• [ ] extra额外的
• [ ] empirical经验；观察
• [ ] ennmerate例举，计数
• [ ] exceed超过，越出
• [ ] expectation期望
• [ ] efficient生效的
• [ ] endow赋予
• [ ] explicitly清楚的
• [ ] exponential family指数家族

• [ ] equivalently等价的

• [ ] F

• [ ] feasible可行的

• [ ] forary初次尝试
• [ ] finite有限的，限定的
• [ ] forgo摒弃，放弃
• [ ] fliter过滤
• [ ] frequentist最常发生的
• [ ] forward search前向式搜索

• [ ] formalize使定形

• [ ] G

• [ ] generalized归纳的

• [ ] generalization概括，归纳；普遍化；判断（根据不足）
• [ ] guarantee保证；抵押品
• [ ] generate形成，产生
• [ ] geometric margins几何边界
• [ ] gap裂口

• [ ] generative生产的；有生产力的

• [ ] H

• [ ] heuristic启发式的；启发法；启发程序

• [ ] hone怀恋；磨

• [ ] hyperplane超平面

• [ ] L

• [ ] initial最初的

• [ ] implement执行
• [ ] intuitive凭直觉获知的
• [ ] incremental增加的
• [900 ] intercept截距
• [ ] intuitious直觉
• [ ] instantiation例子
• [ ] indicator指示物，指示器
• [ ] interative重复的，迭代的
• [ ] integral积分
• [ ] identical相等的；完全相同的
• [ ] indicate表示，指出
• [ ] invariance不变性，恒定性
• [ ] impose把…强加于
• [ ] intermediate中间的

• [ ] interpretation解释，翻译

• [ ] J

• [ ] joint distribution联合概率

• [ ] L

• [ ] lieu替代

• [ ] logarithmic对数的，用对数表示的
• [ ] latent潜在的

• [ ] Leave-one-out cross validation留一法交叉验证

• [ ] M

• [ ] magnitude巨大

• [ ] mapping绘图，制图；映射
• [ ] matrix矩阵
• [ ] mutual相互的，共同的
• [ ] monotonically单调的
• [ ] minor较小的，次要的
• [ ] multinomial多项的

• [ ] multi-class classification二分类问题

• [ ] N

• [ ] nasty讨厌的

• [ ] notation标志，注释

• [ ] naïve朴素的

• [ ] O

• [ ] obtain得到

• [ ] oscillate摆动
• [ ] optimization problem最优化问题
• [ ] objective function目标函数
• [ ] optimal最理想的
• [ ] orthogonal(矢量，矩阵等)正交的
• [ ] orientation方向
• [ ] ordinary普通的

• [ ] occasionally偶然的

• [ ] P

• [ ] partial derivative偏导数

• [ ] property性质
• [ ] proportional成比例的
• [ ] primal原始的，最初的
• [ ] permit允许
• [ ] pseudocode伪代码
• [ ] permissible可允许的
• [ ] polynomial多项式
• [ ] preliminary预备
• [ ] precision精度
• [ ] perturbation 不安，扰乱
• [ ] poist假定，设想
• [ ] positive semi-definite半正定的
• [ ] parentheses圆括号
• [ ] posterior probability后验概率
• [ ] plementarity补充
• [ ] pictorially图像的
• [ ] parameterize确定…的参数
• [ ] poisson distribution柏松分布

• [ ] pertinent相关的

• [ ] Q

• [ ] quadratic二次的

• [ ] quantity量，数量；分量

• [ ] query疑问的

• [ ] R

• [ ] regularization使系统化；调整

• [ ] reoptimize重新优化
• [ ] restrict限制；限定；约束
• [ ] reminiscent回忆往事的；提醒的；使人联想…的（of）
• [ ] remark注意
• [ ] random variable随机变量
• [ ] respect考虑
• [ ] respectively各自的；分别的

• [ ] redundant过多的；冗余的

• [ ] S

• [ ] susceptible敏感的

• [ ] stochastic可能的；随机的
• [ ] symmetric对称的
• [ ] sophisticated复杂的
• [ ] spurious假的；伪造的
• [ ] subtract减去；减法器
• [ ] simultaneously同时发生地；同步地
• [ ] suffice满足
• [ ] scarce稀有的，难得的
• [ ] split分解，分离
• [ ] subset子集
• [ ] statistic统计量
• [ ] successive iteratious连续的迭代
• [ ] scale标度
• [ ] sort of有几分的

• [ ] squares平方

• [ ] T

• [ ] trajectory轨迹

• [ ] temporarily暂时的
• [ ] terminology专用名词
• [ ] tolerance容忍；公差
• [ ] thumb翻阅
• [ ] threshold阈，临界
• [ ] theorem定理

• [ ] tangent正弦

• [ ] U

• [ ] unit-length vector单位向量

• [ ] V

• [ ] valid有效的，正确的

• [ ] variance方差
• [ ] variable变量；变元
• [ ] vocabulary词汇

• [ ] valued经估价的；宝贵的

• [ ] W

• [1038 ] wrapper包装

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