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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