├── README.md └── AI_Glossary.md /README.md: -------------------------------------------------------------------------------- 1 | # AIandRobot 2 | 人工智能和深度学习等相关术语 3 | -------------------------------------------------------------------------------- /AI_Glossary.md: -------------------------------------------------------------------------------- 1 | # 人工智能相关术语（按首字母排序） 2 | 3 | 4 | |缩写|英语|汉语| 5 | |-----|-----|-----| 6 | ||**A**|| 7 | ||Activation Function|激活函数| 8 | ||Adversarial Networks|对抗网络| 9 | ||Affine Layer|仿射层| 10 | ||agent|代理/智能体| 11 | ||algorithm|算法| 12 | ||alpha-beta pruning|α-β剪枝| 13 | ||anomaly detection|异常检测| 14 | ||approximation|近似| 15 | |AGI|Artificial General Intelligence|通用人工智能| 16 | |AI|Artificial Intelligence|人工智能| 17 | ||association analysis|关联分析| 18 | ||attention mechanism|注意机制| 19 | ||autoencoder|自编码器| 20 | |ASR|automatic speech recognition|自动语音识别| 21 | ||automatic summarization|自动摘要| 22 | ||average gradient|平均梯度| 23 | ||Average-Pooling|平均池化| 24 | ||**B**|| 25 | |BP|backpropagation|反向传播| 26 | |BPTT|Backpropagation Through Time|通过时间的反向传播| 27 | |BN|Batch Normalization|分批标准化| 28 | ||Bayesian network|贝叶斯网络| 29 | ||Bias-Variance Dilemma|偏差/方差困境| 30 | |Bi-LSTM|Bi-directional Long-Short Term Memory|双向长短期记忆| 31 | ||bias|偏置/偏差| 32 | ||big data|大数据| 33 | ||Boltzmann machine|玻尔兹曼机| 34 | ||**C**|| 35 | |CPU|Central Processing Unit|中央处理器| 36 | ||chunk|词块| 37 | ||clustering|聚类| 38 | ||cluster analysis|聚类分析| 39 | ||co-adapting|共适应| 40 | ||co-occurrence|共现| 41 | ||Computation Cost|计算成本| 42 | ||Computational Linguistics|计算语言学| 43 | ||computer vision|计算机视觉| 44 | ||concept drift|概念漂移| 45 | |CRF|conditional random field|条件随机域/场| 46 | ||convergence|收敛| 47 | |CA|conversational agent|会话代理| 48 | ||convexity|凸性| 49 | |CNN|convolutional neural network|卷积神经网络| 50 | ||Cost Function|成本函数| 51 | ||cross entropy|交叉熵| 52 | ||**D**|| 53 | ||Decision Boundary|决策边界| 54 | ||Decision Trees|决策树| 55 | |DBN|Deep Belief Network|深度信念网络| 56 | |[DCGAN](https://arxiv.org/abs/1511.06434)|Deep Convolutional Generative Adversarial Network|深度卷积生成对抗网络| 57 | |DL|deep learning|深度学习| 58 | |DNN|deep neural network|深度神经网络| 59 | ||Deep Q-Learning|深度Q学习| 60 | |DQN|Deep Q-Network|深度Q网络| 61 | |[DNC](http://www.nature.com/nature/journal/v538/n7626/full/nature20101.html)|differentiable neural computer|可微分神经计算机| 62 | ||dimensionality reduction algorithm|降维算法| 63 | ||discriminative model|判别模型| 64 | ||discriminator|判别器| 65 | ||divergence|散度| 66 | ||domain adaption|领域自适应| 67 | ||Dropout|| 68 | ||Dynamic Fusion|动态融合| 69 | ||**E**|| 70 | ||Embedding|嵌入| 71 | ||emotional analysis|情绪分析| 72 | ||End-to-End|端到端| 73 | |EM|Expectation-Maximization|期望最大化| 74 | ||Exploding Gradient Problem|梯度爆炸问题| 75 | |[ELM](http://axon.cs.byu.edu/~martinez/classes/678/Presentations/Yao.pdf)|Extreme Learning Machine|超限学习机| 76 | ||**F**|| 77 | |[FAIR](https://research.facebook.com/ai)|Facebook Artificial Intelligence Research|Facebook人工智能研究所| 78 | ||factorization|因子分解| 79 | ||feature engineering| 特征工程| 80 | ||Featured Learning|特征学习| 81 | ||Feedforward Neural Networks|前馈神经网络| 82 | ||**G**|| 83 | ||game theory|博弈论| 84 | |GMM|Gaussian Mixture Model|高斯混合模型| 85 | |GA|Genetic Algorithm|遗传算法| 86 | ||Generalization|泛化| 87 | |[GAN](https://arxiv.org/abs/1406.2661)|Generative Adversarial Networks|生成对抗网络| 88 | ||Generative Model|生成模型| 89 | ||Generator|生成器| 90 | ||Global Optimization|全局优化| 91 | |[GNMT](https://arxiv.org/abs/1609.08144)|Google Neural Machine Translation|谷歌神经机器翻译| 92 | ||Gradient Descent|梯度下降| 93 | ||graph theory|图论| 94 | |GPU|graphics processing unit|图形处理单元/图形处理器| 95 | ||**H**|| 96 | |HDM|hidden dynamic model|隐动态模型| 97 | ||hidden layer|隐藏层| 98 | |HMM|Hidden Markov Model|隐马尔可夫模型| 99 | ||hybrid computing|混合计算| 100 | ||hyperparameter|超参数| 101 | ||**I**|| 102 | |ICA|Independent Component Analysis|独立成分分析| 103 | ||input|输入| 104 | |[ICML](http://icml.cc/)|International Conference for Machine Learning|国际机器学习大会| 105 | ||language phenomena|语言现象| 106 | ||latent dirichlet allocation|隐含狄利克雷分布| 107 | ||**J**|| 108 | |JSD|Jensen-Shannon Divergence|JS距离| 109 | ||**K**|| 110 | ||K-Means Clustering|K-均值聚类| 111 | |K-NN|K-Nearest Neighbours Algorithm|K-最近邻算法| 112 | ||Knowledge Representation|知识表征| 113 | |KB|knowledge base|知识库| 114 | ||**L**|| 115 | ||Latent Dirichlet Allocation|隐狄利克雷分布| 116 | |LSA|latent semantic analysis| 潜在语义分析| 117 | ||learner|学习器| 118 | ||Linear Regression|线性回归| 119 | ||log likelihood|对数似然| 120 | ||Logistic Regression|Logistic回归| 121 | |[LSTM](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf)|Long-Short Term Memory|长短期记忆| 122 | ||loss|损失| 123 | ||**M**|| 124 | |MT|machine translation|机器翻译| 125 | ||Max-Pooling|最大池化| 126 | ||Maximum Likelihood|最大似然| 127 | ||minimax game|最小最大博弈| 128 | ||Momentum|动量| 129 | |MLP|Multilayer Perceptron|多层感知器| 130 | ||multi-document summarization|多文档摘要| 131 | |MLP|multi layered perceptron|多层感知器| 132 | ||multimodal learning|多模态学习| 133 | ||multiple linear regression|多元线性回归| 134 | ||**N**|| 135 | ||Naive Bayes Classifier|朴素贝叶斯分类器| 136 | ||named entity recognition|命名实体识别| 137 | ||Nash equilibrium|纳什均衡| 138 | |NLG|natural language generation|自然语言生成| 139 | |NLP|natural language processing |自然语言处理| 140 | |NLL|Negative Log Likelihood|负对数似然| 141 | |NMT|Neural Machine Translation|神经机器翻译| 142 | |NTM|Neural Turing Machine|神经图灵机| 143 | |NCE|noise-contrastive estimation|噪音对比估计| 144 | ||non-convex optimization|非凸优化| 145 | ||non-negative matrix factorization|非负矩阵分解| 146 | ||Non-Saturating Game|非饱和博弈| 147 | ||**O**|| 148 | ||objective function|目标函数| 149 | ||Off-Policy|离策略| 150 | ||On-Policy|在策略| 151 | ||one shot learning|一次性学习| 152 | ||output|输出| 153 | ||**P**|| 154 | ||Parameter|参数| 155 | ||parse tree|解析树| 156 | ||part-of-speech tagging|词性标注| 157 | |PSO|Particle Swarm Optimization|粒子群优化算法| 158 | ||perceptron|感知器| 159 | ||polarity detection|极性检测| 160 | ||pooling|池化| 161 | |[PPGN](https://arxiv.org/abs/1612.00005)|Plug and Play Generative Network|即插即用生成网络| 162 | |PCA|principal component analysis|主成分分析| 163 | ||Probability Graphical Model|概率图模型| 164 | ||**Q**|| 165 | |[QNN](https://arxiv.org/abs/1609.07061)|Quantized Neural Network|量子化神经网络| 166 | ||quantum computer|量子计算机| 167 | ||Quantum Computing|量子计算| 168 | ||**R**|| 169 | |RBF|Radial Basis Function|径向基函数| 170 | ||Random Forest Algorithm|随机森林算法| 171 | |ReLU|Rectified Linear Unit|线性修正单元/线性修正函数| 172 | |RNN|Recurrent Neural Network|循环神经网络| 173 | ||recursive neural network|递归神经网络| 174 | |RL|reinforcement learning|强化学习| 175 | ||representation|表征| 176 | ||representation learning|表征学习| 177 | ||Residual Mapping|残差映射| 178 | ||Residual Network|残差网络| 179 | |RBM|Restricted Boltzmann Machine|受限玻尔兹曼机| 180 | ||Robot|机器人| 181 | ||Robustness|稳健性| 182 | |RE|Rule Engine|规则引擎| 183 | ||**S**|| 184 | ||saddle point|鞍点| 185 | ||Self-Driving|自动驾驶| 186 | |SOM|self organised map|自组织映射| 187 | ||Semi-Supervised Learning|半监督学习| 188 | ||sentiment analysis| 情感分析| 189 | |SLAM|simultaneous localization and mapping|同步定位与地图构建| 190 | |SVD|Singular Value Decomposition|奇异值分解| 191 | ||Spectral Clustering|谱聚类| 192 | ||Speech Recognition|语音识别| 193 | |SGD|stochastic gradient descent| 随机梯度下降| 194 | ||supervised learning|监督学习| 195 | |SVM|Support Vector Machine|支持向量机| 196 | ||synset|同义词集| 197 | ||**T**|| 198 | |t-SNE|T-Distribution Stochastic Neighbour Embedding|T-分布随机近邻嵌入| 199 | ||tensor|张量| 200 | |TPU|Tensor Processing Units|张量处理单元| 201 | ||the least square method|最小二乘法| 202 | ||Threshold|阙值| 203 | ||Time Step|时间步骤| 204 | ||tokenization|标记化| 205 | ||treebank|树库| 206 | ||transfer learning|迁移学习| 207 | ||Turing Machine|图灵机| 208 | ||**U**|| 209 | ||unsupervised learning|无监督学习| 210 | ||**V**|| 211 | ||Vanishing Gradient Problem|梯度消失问题| 212 | |VC Theory|Vapnik–Chervonenkis theory|万普尼克-泽范兰杰斯理论| 213 | ||von Neumann architecture|冯·诺伊曼架构/结构| 214 | ||**W**|| 215 | |[WGAN](https://arxiv.org/abs/1701.07875)|Wasserstein GAN|| 216 | |W|weight|权重| 217 | ||word embedding|词嵌入| 218 | |WSD|word sense disambiguation|词义消歧| 219 | ||**X**|| 220 | ||**Y**|| 221 | ||**Z**|| 222 | |ZSL|zero-shot learning|零次学习| 223 | ||zero-data learning|零数据学习| 224 | ||**0**|| 225 | --------------------------------------------------------------------------------