{"id":1228,"date":"2021-10-19T17:06:18","date_gmt":"2021-10-19T09:06:18","guid":{"rendered":"https:\/\/aif.amtbbs.org\/?p=1228"},"modified":"2021-10-19T17:06:18","modified_gmt":"2021-10-19T09:06:18","slug":"%e8%bf%99725%e4%b8%aa%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e6%9c%af%e8%af%ad%e8%a1%a8%ef%bc%8c%e5%a4%aa%e5%85%a8%e4%ba%86%ef%bc%81","status":"publish","type":"post","link":"https:\/\/aif.amtbbs.org\/index.php\/2021\/10\/19\/1228\/","title":{"rendered":"\u8fd9725\u4e2a\u673a\u5668\u5b66\u4e60\u672f\u8bed\u8868\uff0c\u592a\u5168\u4e86\uff01"},"content":{"rendered":"
\n

\u4f60\u597d\uff0c\u6211\u662fzhenguo<\/p>\n

\u8fd9\u662f\u51e0\u4f4d\u673a\u5668\u5b66\u4e60\u6743\u5a01\u4e13\u5bb6\u6c47\u603b\u7684725\u4e2a\u673a\u5668\u5b66\u4e60\u672f\u8bed\u8868\uff0c\u975e\u5e38\u5168\u9762\u4e86\uff0c\u503c\u5f97\u6536\u85cf\uff01<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u82f1\u6587\u672f\u8bed<\/strong><\/th>\n\u4e2d\u6587\u7ffb\u8bd1<\/strong><\/th>\n<\/tr>\n<\/thead>\n
0-1 Loss Function<\/td>\n0-1\u635f\u5931\u51fd\u6570<\/td>\n<\/tr>\n
Accept-Reject Sampling Method<\/td>\n\u63a5\u53d7-\u62d2\u7edd\u62bd\u6837\u6cd5\/\u63a5\u53d7-\u62d2\u7edd\u91c7\u6837\u6cd5<\/td>\n<\/tr>\n
Accumulated Error Backpropagation<\/td>\n\u7d2f\u79ef\u8bef\u5dee\u53cd\u5411\u4f20\u64ad<\/td>\n<\/tr>\n
Accuracy<\/td>\n\u7cbe\u5ea6<\/td>\n<\/tr>\n
Acquisition Function<\/td>\n\u91c7\u96c6\u51fd\u6570<\/td>\n<\/tr>\n
Action<\/td>\n\u52a8\u4f5c<\/td>\n<\/tr>\n
Activation Function<\/td>\n\u6fc0\u6d3b\u51fd\u6570<\/td>\n<\/tr>\n
Active Learning<\/td>\n\u4e3b\u52a8\u5b66\u4e60<\/td>\n<\/tr>\n
Adaptive Bitrate Algorithm<\/td>\n\u81ea\u9002\u5e94\u6bd4\u7279\u7387\u7b97\u6cd5<\/td>\n<\/tr>\n
Adaptive Boosting<\/td>\nAdaBoost<\/td>\n<\/tr>\n
Adaptive Gradient Algorithm<\/td>\nAdaGrad<\/td>\n<\/tr>\n
Adaptive Moment Estimation Algorithm<\/td>\nAdam\u7b97\u6cd5<\/td>\n<\/tr>\n
Adaptive Resonance Theory<\/td>\n\u81ea\u9002\u5e94\u8c10\u632f\u7406\u8bba<\/td>\n<\/tr>\n
Additive Model<\/td>\n\u52a0\u6027\u6a21\u578b<\/td>\n<\/tr>\n
Affinity Matrix<\/td>\n\u4eb2\u548c\u77e9\u9635<\/td>\n<\/tr>\n
Agent<\/td>\n\u667a\u80fd\u4f53<\/td>\n<\/tr>\n
Algorithm<\/td>\n\u7b97\u6cd5<\/td>\n<\/tr>\n
Alpha-Beta Pruning<\/td>\n\u03b1-\u03b2\u4fee\u526a\u6cd5<\/td>\n<\/tr>\n
Anomaly Detection<\/td>\n\u5f02\u5e38\u68c0\u6d4b<\/td>\n<\/tr>\n
Approximate Inference<\/td>\n\u8fd1\u4f3c\u63a8\u65ad<\/td>\n<\/tr>\n
Area Under ROC Curve<\/td>\nAUC<\/td>\n<\/tr>\n
Artificial Intelligence<\/td>\n\u4eba\u5de5\u667a\u80fd<\/td>\n<\/tr>\n
Artificial Neural Network<\/td>\n\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc<\/td>\n<\/tr>\n
Artificial Neuron<\/td>\n\u4eba\u5de5\u795e\u7ecf\u5143<\/td>\n<\/tr>\n
Attention<\/td>\n\u6ce8\u610f\u529b<\/td>\n<\/tr>\n
Attention Mechanism<\/td>\n\u6ce8\u610f\u529b\u673a\u5236<\/td>\n<\/tr>\n
Attribute<\/td>\n\u5c5e\u6027<\/td>\n<\/tr>\n
Attribute Space<\/td>\n\u5c5e\u6027\u7a7a\u95f4<\/td>\n<\/tr>\n
Autoencoder<\/td>\n\u81ea\u7f16\u7801\u5668<\/td>\n<\/tr>\n
Automatic Differentiation<\/td>\n\u81ea\u52a8\u5fae\u5206<\/td>\n<\/tr>\n
Autoregressive Model<\/td>\n\u81ea\u56de\u5f52\u6a21\u578b<\/td>\n<\/tr>\n
Back Propagation<\/td>\n\u53cd\u5411\u4f20\u64ad<\/td>\n<\/tr>\n
Back Propagation Algorithm<\/td>\n\u53cd\u5411\u4f20\u64ad\u7b97\u6cd5<\/td>\n<\/tr>\n
Back Propagation Through Time<\/td>\n\u968f\u65f6\u95f4\u53cd\u5411\u4f20\u64ad<\/td>\n<\/tr>\n
Backward Induction<\/td>\n\u53cd\u5411\u5f52\u7eb3<\/td>\n<\/tr>\n
Backward Search<\/td>\n\u53cd\u5411\u641c\u7d22<\/td>\n<\/tr>\n
Bag of Words<\/td>\n\u8bcd\u888b<\/td>\n<\/tr>\n
Bandit<\/td>\n\u8d4c\u535a\u673a\/\u8001\u864e\u673a<\/td>\n<\/tr>\n
Base Learner<\/td>\n\u57fa\u5b66\u4e60\u5668<\/td>\n<\/tr>\n
Base Learning Algorithm<\/td>\n\u57fa\u5b66\u4e60\u7b97\u6cd5<\/td>\n<\/tr>\n
Baseline<\/td>\n\u57fa\u51c6<\/td>\n<\/tr>\n
Batch<\/td>\n\u6279\u91cf<\/td>\n<\/tr>\n
Batch Normalization<\/td>\n\u6279\u91cf\u89c4\u8303\u5316<\/td>\n<\/tr>\n
Bayes Decision Rule<\/td>\n\u8d1d\u53f6\u65af\u51b3\u7b56\u51c6\u5219<\/td>\n<\/tr>\n
Bayes Model Averaging<\/td>\n\u8d1d\u53f6\u65af\u6a21\u578b\u5e73\u5747<\/td>\n<\/tr>\n
Bayes Optimal Classifier<\/td>\n\u8d1d\u53f6\u65af\u6700\u4f18\u5206\u7c7b\u5668<\/td>\n<\/tr>\n
Bayes’ Theorem<\/td>\n\u8d1d\u53f6\u65af\u5b9a\u7406<\/td>\n<\/tr>\n
Bayesian Decision Theory<\/td>\n\u8d1d\u53f6\u65af\u51b3\u7b56\u7406\u8bba<\/td>\n<\/tr>\n
Bayesian Inference<\/td>\n\u8d1d\u53f6\u65af\u63a8\u65ad<\/td>\n<\/tr>\n
Bayesian Learning<\/td>\n\u8d1d\u53f6\u65af\u5b66\u4e60<\/td>\n<\/tr>\n
Bayesian Network<\/td>\n\u8d1d\u53f6\u65af\u7f51\/\u8d1d\u53f6\u65af\u7f51\u7edc<\/td>\n<\/tr>\n
Bayesian Optimization<\/td>\n\u8d1d\u53f6\u65af\u4f18\u5316<\/td>\n<\/tr>\n
Beam Search<\/td>\n\u675f\u641c\u7d22<\/td>\n<\/tr>\n
Benchmark<\/td>\n\u57fa\u51c6<\/td>\n<\/tr>\n
Belief Network<\/td>\n\u4fe1\u5ff5\u7f51\/\u4fe1\u5ff5\u7f51\u7edc<\/td>\n<\/tr>\n
Belief Propagation<\/td>\n\u4fe1\u5ff5\u4f20\u64ad<\/td>\n<\/tr>\n
Bellman Equation<\/td>\n\u8d1d\u5c14\u66fc\u65b9\u7a0b<\/td>\n<\/tr>\n
Bernoulli Distribution<\/td>\n\u4f2f\u52aa\u5229\u5206\u5e03<\/td>\n<\/tr>\n
Beta Distribution<\/td>\n\u8d1d\u5854\u5206\u5e03<\/td>\n<\/tr>\n
Between-Class Scatter Matrix<\/td>\n\u7c7b\u95f4\u6563\u5ea6\u77e9\u9635<\/td>\n<\/tr>\n
BFGS<\/td>\nBFGS<\/td>\n<\/tr>\n
Bias<\/td>\n\u504f\u5dee\/\u504f\u7f6e<\/td>\n<\/tr>\n
Bias In Affine Function<\/td>\n\u504f\u7f6e<\/td>\n<\/tr>\n
Bias In Statistics<\/td>\n\u504f\u5dee<\/td>\n<\/tr>\n
Bias Shift<\/td>\n\u504f\u7f6e\u504f\u79fb<\/td>\n<\/tr>\n
Bias-Variance Decomposition<\/td>\n\u504f\u5dee – \u65b9\u5dee\u5206\u89e3<\/td>\n<\/tr>\n
Bias-Variance Dilemma<\/td>\n\u504f\u5dee – \u65b9\u5dee\u56f0\u5883<\/td>\n<\/tr>\n
Bidirectional Recurrent Neural Network<\/td>\n\u53cc\u5411\u5faa\u73af\u795e\u7ecf\u7f51\u7edc<\/td>\n<\/tr>\n
Bigram<\/td>\n\u4e8c\u5143\u8bed\u6cd5<\/td>\n<\/tr>\n
Bilingual Evaluation Understudy<\/td>\nBLEU<\/td>\n<\/tr>\n
Binary Classification<\/td>\n\u4e8c\u5206\u7c7b<\/td>\n<\/tr>\n
Binomial Distribution<\/td>\n\u4e8c\u9879\u5206\u5e03<\/td>\n<\/tr>\n
Binomial Test<\/td>\n\u4e8c\u9879\u68c0\u9a8c<\/td>\n<\/tr>\n
Boltzmann Distribution<\/td>\n\u73bb\u5c14\u5179\u66fc\u5206\u5e03<\/td>\n<\/tr>\n
Boltzmann Machine<\/td>\n\u73bb\u5c14\u5179\u66fc\u673a<\/td>\n<\/tr>\n
Boosting<\/td>\nBoosting<\/td>\n<\/tr>\n
Bootstrap Aggregating<\/td>\nBagging<\/td>\n<\/tr>\n
Bootstrap Sampling<\/td>\n\u81ea\u52a9\u91c7\u6837\u6cd5<\/td>\n<\/tr>\n
Bootstrapping<\/td>\n\u81ea\u52a9\u6cd5\/\u81ea\u4e3e\u6cd5<\/td>\n<\/tr>\n
Break-Event Point<\/td>\n\u5e73\u8861\u70b9<\/td>\n<\/tr>\n
Bucketing<\/td>\n\u5206\u6876<\/td>\n<\/tr>\n
Calculus of Variations<\/td>\n\u53d8\u5206\u6cd5<\/td>\n<\/tr>\n
Cascade-Correlation<\/td>\n\u7ea7\u8054\u76f8\u5173<\/td>\n<\/tr>\n
Catastrophic Forgetting<\/td>\n\u707e\u96be\u6027\u9057\u5fd8<\/td>\n<\/tr>\n
Categorical Distribution<\/td>\n\u7c7b\u522b\u5206\u5e03<\/td>\n<\/tr>\n
Cell<\/td>\n\u5355\u5143<\/td>\n<\/tr>\n
Chain Rule<\/td>\n\u94fe\u5f0f\u6cd5\u5219<\/td>\n<\/tr>\n
Chebyshev Distance<\/td>\n\u5207\u6bd4\u96ea\u592b\u8ddd\u79bb<\/td>\n<\/tr>\n
Class<\/td>\n\u7c7b\u522b<\/td>\n<\/tr>\n
Class-Imbalance<\/td>\n\u7c7b\u522b\u4e0d\u5e73\u8861<\/td>\n<\/tr>\n
Classification<\/td>\n\u5206\u7c7b<\/td>\n<\/tr>\n
Classification And Regression Tree<\/td>\n\u5206\u7c7b\u4e0e\u56de\u5f52\u6811<\/td>\n<\/tr>\n
Classifier<\/td>\n\u5206\u7c7b\u5668<\/td>\n<\/tr>\n
Clique<\/td>\n\u56e2<\/td>\n<\/tr>\n
Cluster<\/td>\n\u7c07<\/td>\n<\/tr>\n
Cluster Assumption<\/td>\n\u805a\u7c7b\u5047\u8bbe<\/td>\n<\/tr>\n
Clustering<\/td>\n\u805a\u7c7b<\/td>\n<\/tr>\n
Clustering Ensemble<\/td>\n\u805a\u7c7b\u96c6\u6210<\/td>\n<\/tr>\n
Co-Training<\/td>\n\u534f\u540c\u8bad\u7ec3<\/td>\n<\/tr>\n
Coding Matrix<\/td>\n\u7f16\u7801\u77e9\u9635<\/td>\n<\/tr>\n
Collaborative Filtering<\/td>\n\u534f\u540c\u8fc7\u6ee4<\/td>\n<\/tr>\n
Competitive Learning<\/td>\n\u7ade\u4e89\u578b\u5b66\u4e60<\/td>\n<\/tr>\n
Comprehensibility<\/td>\n\u53ef\u89e3\u91ca\u6027<\/td>\n<\/tr>\n
Computation Graph<\/td>\n\u8ba1\u7b97\u56fe<\/td>\n<\/tr>\n
Computational Learning Theory<\/td>\n\u8ba1\u7b97\u5b66\u4e60\u7406\u8bba<\/td>\n<\/tr>\n
Conditional Entropy<\/td>\n\u6761\u4ef6\u71b5<\/td>\n<\/tr>\n
Conditional Probability<\/td>\n\u6761\u4ef6\u6982\u7387<\/td>\n<\/tr>\n
Conditional Probability Distribution<\/td>\n\u6761\u4ef6\u6982\u7387\u5206\u5e03<\/td>\n<\/tr>\n
Conditional Random Field<\/td>\n\u6761\u4ef6\u968f\u673a\u573a<\/td>\n<\/tr>\n
Conditional Risk<\/td>\n\u6761\u4ef6\u98ce\u9669<\/td>\n<\/tr>\n
Confidence<\/td>\n\u7f6e\u4fe1\u5ea6<\/td>\n<\/tr>\n
Confusion Matrix<\/td>\n\u6df7\u6dc6\u77e9\u9635<\/td>\n<\/tr>\n
Conjugate Distribution<\/td>\n\u5171\u8f6d\u5206\u5e03<\/td>\n<\/tr>\n
Connection Weight<\/td>\n\u8fde\u63a5\u6743<\/td>\n<\/tr>\n
Connectionism<\/td>\n\u8fde\u63a5\u4e3b\u4e49<\/td>\n<\/tr>\n
Consistency<\/td>\n\u4e00\u81f4\u6027<\/td>\n<\/tr>\n
Constrained Optimization<\/td>\n\u7ea6\u675f\u4f18\u5316<\/td>\n<\/tr>\n
Context Variable<\/td>\n\u4e0a\u4e0b\u6587\u53d8\u91cf<\/td>\n<\/tr>\n
Context Vector<\/td>\n\u4e0a\u4e0b\u6587\u5411\u91cf<\/td>\n<\/tr>\n
Context Window<\/td>\n\u4e0a\u4e0b\u6587\u7a97\u53e3<\/td>\n<\/tr>\n
Context Word<\/td>\n\u4e0a\u4e0b\u6587\u8bcd<\/td>\n<\/tr>\n
Contextual Bandit<\/td>\n\u4e0a\u4e0b\u6587\u8d4c\u535a\u673a\/\u4e0a\u4e0b\u6587\u8001\u864e\u673a<\/td>\n<\/tr>\n
Contingency Table<\/td>\n\u5217\u8054\u8868<\/td>\n<\/tr>\n
Continuous Attribute<\/td>\n\u8fde\u7eed\u5c5e\u6027<\/td>\n<\/tr>\n
Contrastive Divergence<\/td>\n\u5bf9\u6bd4\u6563\u5ea6<\/td>\n<\/tr>\n
Convergence<\/td>\n\u6536\u655b<\/td>\n<\/tr>\n
Convex Optimization<\/td>\n\u51f8\u4f18\u5316<\/td>\n<\/tr>\n
Convex Quadratic Programming<\/td>\n\u51f8\u4e8c\u6b21\u89c4\u5212<\/td>\n<\/tr>\n
Convolution<\/td>\n\u5377\u79ef<\/td>\n<\/tr>\n
Convolutional Kernel<\/td>\n\u5377\u79ef\u6838<\/td>\n<\/tr>\n
Convolutional Neural Network<\/td>\n\u5377\u79ef\u795e\u7ecf\u7f51\u7edc<\/td>\n<\/tr>\n
Coordinate Descent<\/td>\n\u5750\u6807\u4e0b\u964d<\/td>\n<\/tr>\n
Corpus<\/td>\n\u8bed\u6599\u5e93<\/td>\n<\/tr>\n
Correlation Coefficient<\/td>\n\u76f8\u5173\u7cfb\u6570<\/td>\n<\/tr>\n
Cosine Similarity<\/td>\n\u4f59\u5f26\u76f8\u4f3c\u5ea6<\/td>\n<\/tr>\n
Cost<\/td>\n\u4ee3\u4ef7<\/td>\n<\/tr>\n
Cost Curve<\/td>\n\u4ee3\u4ef7\u66f2\u7ebf<\/td>\n<\/tr>\n
Cost Function<\/td>\n\u4ee3\u4ef7\u51fd\u6570<\/td>\n<\/tr>\n
Cost Matrix<\/td>\n\u4ee3\u4ef7\u77e9\u9635<\/td>\n<\/tr>\n
Cost-Sensitive<\/td>\n\u4ee3\u4ef7\u654f\u611f<\/td>\n<\/tr>\n
Covariance<\/td>\n\u534f\u65b9\u5dee<\/td>\n<\/tr>\n
Covariance Matrix<\/td>\n\u534f\u65b9\u5dee\u77e9\u9635<\/td>\n<\/tr>\n
Critical Point<\/td>\n\u4e34\u754c\u70b9<\/td>\n<\/tr>\n
Cross Entropy<\/td>\n\u4ea4\u53c9\u71b5<\/td>\n<\/tr>\n
Cross Validation<\/td>\n\u4ea4\u53c9\u9a8c\u8bc1<\/td>\n<\/tr>\n
Curse of Dimensionality<\/td>\n\u7ef4\u6570\u707e\u96be<\/td>\n<\/tr>\n
Cutting Plane Algorithm<\/td>\n\u5272\u5e73\u9762\u6cd5<\/td>\n<\/tr>\n
Data Mining<\/td>\n\u6570\u636e\u6316\u6398<\/td>\n<\/tr>\n
Data Set<\/td>\n\u6570\u636e\u96c6<\/td>\n<\/tr>\n
Davidon-Fletcher-Powell<\/td>\nDFP<\/td>\n<\/tr>\n
Decision Boundary<\/td>\n\u51b3\u7b56\u8fb9\u754c<\/td>\n<\/tr>\n
Decision Function<\/td>\n\u51b3\u7b56\u51fd\u6570<\/td>\n<\/tr>\n
Decision Stump<\/td>\n\u51b3\u7b56\u6811\u6869<\/td>\n<\/tr>\n
Decision Tree<\/td>\n\u51b3\u7b56\u6811<\/td>\n<\/tr>\n
Decoder<\/td>\n\u89e3\u7801\u5668<\/td>\n<\/tr>\n
Decoding<\/td>\n\u89e3\u7801<\/td>\n<\/tr>\n
Deconvolution<\/td>\n\u53cd\u5377\u79ef<\/td>\n<\/tr>\n
Deconvolutional Network<\/td>\n\u53cd\u5377\u79ef\u7f51\u7edc<\/td>\n<\/tr>\n
Deduction<\/td>\n\u6f14\u7ece<\/td>\n<\/tr>\n
Deep Belief Network<\/td>\n\u6df1\u5ea6\u4fe1\u5ff5\u7f51\u7edc<\/td>\n<\/tr>\n
Deep Boltzmann Machine<\/td>\n\u6df1\u5ea6\u73bb\u5c14\u5179\u66fc\u673a<\/td>\n<\/tr>\n
Deep Convolutional Generative Adversarial Network<\/td>\n\u6df1\u5ea6\u5377\u79ef\u751f\u6210\u5bf9\u6297\u7f51\u7edc<\/td>\n<\/tr>\n
Deep Learning<\/td>\n\u6df1\u5ea6\u5b66\u4e60<\/td>\n<\/tr>\n
Deep Neural Network<\/td>\n\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc<\/td>\n<\/tr>\n
Deep Q-Network<\/td>\n\u6df1\u5ea6Q\u7f51\u7edc<\/td>\n<\/tr>\n
Delta-Bar-Delta<\/td>\nDelta-Bar-Delta<\/td>\n<\/tr>\n
Denoising<\/td>\n\u53bb\u566a<\/td>\n<\/tr>\n
Denoising Autoencoder<\/td>\n\u53bb\u566a\u81ea\u7f16\u7801\u5668<\/td>\n<\/tr>\n
Denoising Score Matching<\/td>\n\u53bb\u8e81\u5206\u6570\u5339\u914d<\/td>\n<\/tr>\n
Density Estimation<\/td>\n\u5bc6\u5ea6\u4f30\u8ba1<\/td>\n<\/tr>\n
Density-Based Clustering<\/td>\n\u5bc6\u5ea6\u805a\u7c7b<\/td>\n<\/tr>\n
Derivative<\/td>\n\u5bfc\u6570<\/td>\n<\/tr>\n
Determinant<\/td>\n\u884c\u5217\u5f0f<\/td>\n<\/tr>\n
Diagonal Matrix<\/td>\n\u5bf9\u89d2\u77e9\u9635<\/td>\n<\/tr>\n
Dictionary Learning<\/td>\n\u5b57\u5178\u5b66\u4e60<\/td>\n<\/tr>\n
Dimension Reduction<\/td>\n\u964d\u7ef4<\/td>\n<\/tr>\n
Directed Edge<\/td>\n\u6709\u5411\u8fb9<\/td>\n<\/tr>\n
Directed Graphical Model<\/td>\n\u6709\u5411\u56fe\u6a21\u578b<\/td>\n<\/tr>\n
Directed Separation<\/td>\n\u6709\u5411\u5206\u79bb<\/td>\n<\/tr>\n
Dirichlet Distribution<\/td>\n\u72c4\u5229\u514b\u96f7\u5206\u5e03<\/td>\n<\/tr>\n
Discriminative Model<\/td>\n\u5224\u522b\u5f0f\u6a21\u578b<\/td>\n<\/tr>\n
Discriminator<\/td>\n\u5224\u522b\u5668<\/td>\n<\/tr>\n
Discriminator Network<\/td>\n\u5224\u522b\u7f51\u7edc<\/td>\n<\/tr>\n
Distance Measure<\/td>\n\u8ddd\u79bb\u5ea6\u91cf<\/td>\n<\/tr>\n
Distance Metric Learning<\/td>\n\u8ddd\u79bb\u5ea6\u91cf\u5b66\u4e60<\/td>\n<\/tr>\n
Distributed Representation<\/td>\n\u5206\u5e03\u5f0f\u8868\u793a<\/td>\n<\/tr>\n
Diverge<\/td>\n\u53d1\u6563<\/td>\n<\/tr>\n
Divergence<\/td>\n\u6563\u5ea6<\/td>\n<\/tr>\n
Diversity<\/td>\n\u591a\u6837\u6027<\/td>\n<\/tr>\n
Diversity Measure<\/td>\n\u591a\u6837\u6027\u5ea6\u91cf\/\u5dee\u5f02\u6027\u5ea6\u91cf<\/td>\n<\/tr>\n
Domain Adaptation<\/td>\n\u9886\u57df\u81ea\u9002\u5e94<\/td>\n<\/tr>\n
Dominant Strategy<\/td>\n\u4e3b\u7279\u5f81\u503c<\/td>\n<\/tr>\n
Dominant Strategy<\/td>\n\u5360\u4f18\u7b56\u7565<\/td>\n<\/tr>\n
Down Sampling<\/td>\n\u4e0b\u91c7\u6837<\/td>\n<\/tr>\n
Dropout<\/td>\n\u6682\u9000\u6cd5<\/td>\n<\/tr>\n
Dropout Boosting<\/td>\n\u6682\u9000Boosting<\/td>\n<\/tr>\n
Dropout Method<\/td>\n\u6682\u9000\u6cd5<\/td>\n<\/tr>\n
Dual Problem<\/td>\n\u5bf9\u5076\u95ee\u9898<\/td>\n<\/tr>\n
Dummy Node<\/td>\n\u54d1\u7ed3\u70b9<\/td>\n<\/tr>\n
Dynamic Bayesian Network<\/td>\n\u52a8\u6001\u8d1d\u53f6\u65af\u7f51\u7edc<\/td>\n<\/tr>\n
Dynamic Programming<\/td>\n\u52a8\u6001\u89c4\u5212<\/td>\n<\/tr>\n
Early Stopping<\/td>\n\u65e9\u505c<\/td>\n<\/tr>\n
Eigendecomposition<\/td>\n\u7279\u5f81\u5206\u89e3<\/td>\n<\/tr>\n
Eigenvalue<\/td>\n\u7279\u5f81\u503c<\/td>\n<\/tr>\n
Element-Wise Product<\/td>\n\u9010\u5143\u7d20\u79ef<\/td>\n<\/tr>\n
Embedding<\/td>\n\u5d4c\u5165<\/td>\n<\/tr>\n
Empirical Conditional Entropy<\/td>\n\u7ecf\u9a8c\u6761\u4ef6\u71b5<\/td>\n<\/tr>\n
Empirical Distribution<\/td>\n\u7ecf\u9a8c\u5206\u5e03<\/td>\n<\/tr>\n
Empirical Entropy<\/td>\n\u7ecf\u9a8c\u71b5<\/td>\n<\/tr>\n
Empirical Error<\/td>\n\u7ecf\u9a8c\u8bef\u5dee<\/td>\n<\/tr>\n
Empirical Risk<\/td>\n\u7ecf\u9a8c\u98ce\u9669<\/td>\n<\/tr>\n
Empirical Risk Minimization<\/td>\n\u7ecf\u9a8c\u98ce\u9669\u6700\u5c0f\u5316<\/td>\n<\/tr>\n
Encoder<\/td>\n\u7f16\u7801\u5668<\/td>\n<\/tr>\n
Encoding<\/td>\n\u7f16\u7801<\/td>\n<\/tr>\n
End-To-End<\/td>\n\u7aef\u5230\u7aef<\/td>\n<\/tr>\n
Energy Function<\/td>\n\u80fd\u91cf\u51fd\u6570<\/td>\n<\/tr>\n
Energy-Based Model<\/td>\n\u57fa\u4e8e\u80fd\u91cf\u7684\u6a21\u578b<\/td>\n<\/tr>\n
Ensemble Learning<\/td>\n\u96c6\u6210\u5b66\u4e60<\/td>\n<\/tr>\n
Ensemble Pruning<\/td>\n\u96c6\u6210\u4fee\u526a<\/td>\n<\/tr>\n
Entropy<\/td>\n\u71b5<\/td>\n<\/tr>\n
Episode<\/td>\n\u56de\u5408<\/td>\n<\/tr>\n
Epoch<\/td>\n\u8f6e<\/td>\n<\/tr>\n
Error<\/td>\n\u8bef\u5dee<\/td>\n<\/tr>\n
Error Backpropagation Algorithm<\/td>\n\u8bef\u5dee\u53cd\u5411\u4f20\u64ad\u7b97\u6cd5<\/td>\n<\/tr>\n
Error Backpropagation<\/td>\n\u8bef\u5dee\u53cd\u5411\u4f20\u64ad<\/td>\n<\/tr>\n
Error Correcting Output Codes<\/td>\n\u7ea0\u9519\u8f93\u51fa\u7f16\u7801<\/td>\n<\/tr>\n
Error Rate<\/td>\n\u9519\u8bef\u7387<\/td>\n<\/tr>\n
Error-Ambiguity Decomposition<\/td>\n\u8bef\u5dee\uff0d\u5206\u6b67\u5206\u89e3<\/td>\n<\/tr>\n
Estimator<\/td>\n\u4f30\u8ba1\/\u4f30\u8ba1\u91cf<\/td>\n<\/tr>\n
Euclidean Distance<\/td>\n\u6b27\u6c0f\u8ddd\u79bb<\/td>\n<\/tr>\n
Evidence<\/td>\n\u8bc1\u636e<\/td>\n<\/tr>\n
Evidence Lower Bound<\/td>\n\u8bc1\u636e\u4e0b\u754c<\/td>\n<\/tr>\n
Exact Inference<\/td>\n\u7cbe\u786e\u63a8\u65ad<\/td>\n<\/tr>\n
Example<\/td>\n\u6837\u4f8b<\/td>\n<\/tr>\n
Expectation<\/td>\n\u671f\u671b<\/td>\n<\/tr>\n
Expectation Maximization<\/td>\n\u671f\u671b\u6700\u5927\u5316<\/td>\n<\/tr>\n
Expected Loss<\/td>\n\u671f\u671b\u635f\u5931<\/td>\n<\/tr>\n
Expert System<\/td>\n\u4e13\u5bb6\u7cfb\u7edf<\/td>\n<\/tr>\n
Exploding Gradient<\/td>\n\u68af\u5ea6\u7206\u70b8<\/td>\n<\/tr>\n
Exponential Loss Function<\/td>\n\u6307\u6570\u635f\u5931\u51fd\u6570<\/td>\n<\/tr>\n
Factor<\/td>\n\u56e0\u5b50<\/td>\n<\/tr>\n
Factorization<\/td>\n\u56e0\u5b50\u5206\u89e3<\/td>\n<\/tr>\n
Feature<\/td>\n\u7279\u5f81<\/td>\n<\/tr>\n
Feature Engineering<\/td>\n\u7279\u5f81\u5de5\u7a0b<\/td>\n<\/tr>\n
Feature Map<\/td>\n\u7279\u5f81\u56fe<\/td>\n<\/tr>\n
Feature Selection<\/td>\n\u7279\u5f81\u9009\u62e9<\/td>\n<\/tr>\n
Feature Vector<\/td>\n\u7279\u5f81\u5411\u91cf<\/td>\n<\/tr>\n
Featured Learning<\/td>\n\u7279\u5f81\u5b66\u4e60<\/td>\n<\/tr>\n
Feedforward<\/td>\n\u524d\u9988<\/td>\n<\/tr>\n
Feedforward Neural Network<\/td>\n\u524d\u9988\u795e\u7ecf\u7f51\u7edc<\/td>\n<\/tr>\n
Few-Shot Learning<\/td>\n\u5c11\u8bd5\u5b66\u4e60<\/td>\n<\/tr>\n
Filter<\/td>\n\u6ee4\u6ce2\u5668<\/td>\n<\/tr>\n
Fine-Tuning<\/td>\n\u5fae\u8c03<\/td>\n<\/tr>\n
Fluctuation<\/td>\n\u632f\u8361<\/td>\n<\/tr>\n
Forget Gate<\/td>\n\u9057\u5fd8\u95e8<\/td>\n<\/tr>\n
Forward Propagation<\/td>\n\u524d\u5411\u4f20\u64ad\/\u6b63\u5411\u4f20\u64ad<\/td>\n<\/tr>\n
Forward Stagewise Algorithm<\/td>\n\u524d\u5411\u5206\u6b65\u7b97\u6cd5<\/td>\n<\/tr>\n
Fractionally Strided Convolution<\/td>\n\u5fae\u6b65\u5377\u79ef<\/td>\n<\/tr>\n
Frobenius Norm<\/td>\nFrobenius \u8303\u6570<\/td>\n<\/tr>\n
Full Padding<\/td>\n\u5168\u586b\u5145<\/td>\n<\/tr>\n
Functional<\/td>\n\u6cdb\u51fd<\/td>\n<\/tr>\n
Functional Neuron<\/td>\n\u529f\u80fd\u795e\u7ecf\u5143<\/td>\n<\/tr>\n
Gated Recurrent Unit<\/td>\n\u95e8\u63a7\u5faa\u73af\u5355\u5143<\/td>\n<\/tr>\n
Gated RNN<\/td>\n\u95e8\u63a7RNN<\/td>\n<\/tr>\n
Gaussian Distribution<\/td>\n\u9ad8\u65af\u5206\u5e03<\/td>\n<\/tr>\n
Gaussian Kernel<\/td>\n\u9ad8\u65af\u6838<\/td>\n<\/tr>\n
Gaussian Kernel Function<\/td>\n\u9ad8\u65af\u6838\u51fd\u6570<\/td>\n<\/tr>\n
Gaussian Mixture Model<\/td>\n\u9ad8\u65af\u6df7\u5408\u6a21\u578b<\/td>\n<\/tr>\n
Gaussian Process<\/td>\n\u9ad8\u65af\u8fc7\u7a0b<\/td>\n<\/tr>\n
Generalization Ability<\/td>\n\u6cdb\u5316\u80fd\u529b<\/td>\n<\/tr>\n
Generalization Error<\/td>\n\u6cdb\u5316\u8bef\u5dee<\/td>\n<\/tr>\n
Generalization Error Bound<\/td>\n\u6cdb\u5316\u8bef\u5dee\u4e0a\u754c<\/td>\n<\/tr>\n
Generalize<\/td>\n\u6cdb\u5316<\/td>\n<\/tr>\n
Generalized Lagrange Function<\/td>\n\u5e7f\u4e49\u62c9\u683c\u6717\u65e5\u51fd\u6570<\/td>\n<\/tr>\n
Generalized Linear Model<\/td>\n\u5e7f\u4e49\u7ebf\u6027\u6a21\u578b<\/td>\n<\/tr>\n
Generalized Rayleigh Quotient<\/td>\n\u5e7f\u4e49\u745e\u5229\u5546<\/td>\n<\/tr>\n
Generative Adversarial Network<\/td>\n\u751f\u6210\u5bf9\u6297\u7f51\u7edc<\/td>\n<\/tr>\n
Generative Model<\/td>\n\u751f\u6210\u5f0f\u6a21\u578b<\/td>\n<\/tr>\n
Generator<\/td>\n\u751f\u6210\u5668<\/td>\n<\/tr>\n
Generator Network<\/td>\n\u751f\u6210\u5668\u7f51\u7edc<\/td>\n<\/tr>\n
Genetic Algorithm<\/td>\n\u9057\u4f20\u7b97\u6cd5<\/td>\n<\/tr>\n
Gibbs Distribution<\/td>\n\u5409\u5e03\u65af\u5206\u5e03<\/td>\n<\/tr>\n
Gibbs Sampling<\/td>\n\u5409\u5e03\u65af\u91c7\u6837\/\u5409\u5e03\u65af\u62bd\u6837<\/td>\n<\/tr>\n
Gini Index<\/td>\n\u57fa\u5c3c\u6307\u6570<\/td>\n<\/tr>\n
Global Markov Property<\/td>\n\u5168\u5c40\u9a6c\u5c14\u53ef\u592b\u6027<\/td>\n<\/tr>\n
Global Minimum<\/td>\n\u5168\u5c40\u6700\u5c0f<\/td>\n<\/tr>\n
Gradient<\/td>\n\u68af\u5ea6<\/td>\n<\/tr>\n
Gradient Clipping<\/td>\n\u68af\u5ea6\u622a\u65ad<\/td>\n<\/tr>\n
Gradient Descent<\/td>\n\u68af\u5ea6\u4e0b\u964d<\/td>\n<\/tr>\n
Gradient Descent Method<\/td>\n\u68af\u5ea6\u4e0b\u964d\u6cd5<\/td>\n<\/tr>\n
Gradient Exploding Problem<\/td>\n\u68af\u5ea6\u7206\u70b8\u95ee\u9898<\/td>\n<\/tr>\n
Gram Matrix<\/td>\nGram \u77e9\u9635<\/td>\n<\/tr>\n
Graph Convolutional Network<\/td>\n\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\/\u56fe\u5377\u79ef\u7f51\u7edc<\/td>\n<\/tr>\n
Graph Neural Network<\/td>\n\u56fe\u795e\u7ecf\u7f51\u7edc<\/td>\n<\/tr>\n
Graphical Model<\/td>\n\u56fe\u6a21\u578b<\/td>\n<\/tr>\n
Grid Search<\/td>\n\u7f51\u683c\u641c\u7d22<\/td>\n<\/tr>\n
Ground Truth<\/td>\n\u771f\u5b9e\u503c<\/td>\n<\/tr>\n
Hadamard Product<\/td>\nHadamard\u79ef<\/td>\n<\/tr>\n
Hamming Distance<\/td>\n\u6c49\u660e\u8ddd\u79bb<\/td>\n<\/tr>\n
Hard Margin<\/td>\n\u786c\u95f4\u9694<\/td>\n<\/tr>\n
Hebbian Rule<\/td>\n\u8d6b\u5e03\u6cd5\u5219<\/td>\n<\/tr>\n
Hidden Layer<\/td>\n\u9690\u85cf\u5c42<\/td>\n<\/tr>\n
Hidden Markov Model<\/td>\n\u9690\u9a6c\u5c14\u53ef\u592b\u6a21\u578b<\/td>\n<\/tr>\n
Hidden Variable<\/td>\n\u9690\u53d8\u91cf<\/td>\n<\/tr>\n
Hierarchical Clustering<\/td>\n\u5c42\u6b21\u805a\u7c7b<\/td>\n<\/tr>\n
Hilbert Space<\/td>\n\u5e0c\u5c14\u4f2f\u7279\u7a7a\u95f4<\/td>\n<\/tr>\n
Hinge Loss Function<\/td>\n\u5408\u9875\u635f\u5931\u51fd\u6570\/Hinge\u635f\u5931\u51fd\u6570<\/td>\n<\/tr>\n
Hold-Out<\/td>\n\u7559\u51fa\u6cd5<\/td>\n<\/tr>\n
Hyperparameter<\/td>\n\u8d85\u53c2\u6570<\/td>\n<\/tr>\n
Hyperparameter Optimization<\/td>\n\u8d85\u53c2\u6570\u4f18\u5316<\/td>\n<\/tr>\n
Hypothesis<\/td>\n\u5047\u8bbe<\/td>\n<\/tr>\n
Hypothesis Space<\/td>\n\u5047\u8bbe\u7a7a\u95f4<\/td>\n<\/tr>\n
Hypothesis Test<\/td>\n\u5047\u8bbe\u68c0\u9a8c<\/td>\n<\/tr>\n
Identity Matrix<\/td>\n\u5355\u4f4d\u77e9\u9635<\/td>\n<\/tr>\n
Imitation Learning<\/td>\n\u6a21\u4eff\u5b66\u4e60<\/td>\n<\/tr>\n
Importance Sampling<\/td>\n\u91cd\u8981\u6027\u91c7\u6837<\/td>\n<\/tr>\n
Improved Iterative Scaling<\/td>\n\u6539\u8fdb\u7684\u8fed\u4ee3\u5c3a\u5ea6\u6cd5<\/td>\n<\/tr>\n
Incremental Learning<\/td>\n\u589e\u91cf\u5b66\u4e60<\/td>\n<\/tr>\n
Independent and Identically Distributed<\/td>\n\u72ec\u7acb\u540c\u5206\u5e03<\/td>\n<\/tr>\n
Indicator Function<\/td>\n\u6307\u793a\u51fd\u6570<\/td>\n<\/tr>\n
Individual Learner<\/td>\n\u4e2a\u4f53\u5b66\u4e60\u5668<\/td>\n<\/tr>\n
Induction<\/td>\n\u5f52\u7eb3<\/td>\n<\/tr>\n
Inductive Bias<\/td>\n\u5f52\u7eb3\u504f\u597d<\/td>\n<\/tr>\n
Inductive Learning<\/td>\n\u5f52\u7eb3\u5b66\u4e60<\/td>\n<\/tr>\n
Inductive Logic Programming<\/td>\n\u5f52\u7eb3\u903b\u8f91\u7a0b\u5e8f\u8bbe\u8ba1<\/td>\n<\/tr>\n
Inference<\/td>\n\u63a8\u65ad<\/td>\n<\/tr>\n
Information Entropy<\/td>\n\u4fe1\u606f\u71b5<\/td>\n<\/tr>\n
Information Gain<\/td>\n\u4fe1\u606f\u589e\u76ca<\/td>\n<\/tr>\n
Inner Product<\/td>\n\u5185\u79ef<\/td>\n<\/tr>\n
Instance<\/td>\n\u793a\u4f8b<\/td>\n<\/tr>\n
Internal Covariate Shift<\/td>\n\u5185\u90e8\u534f\u53d8\u91cf\u504f\u79fb<\/td>\n<\/tr>\n
Inverse Matrix<\/td>\n\u9006\u77e9\u9635<\/td>\n<\/tr>\n
Inverse Resolution<\/td>\n\u9006\u5f52\u7ed3<\/td>\n<\/tr>\n
Isometric Mapping<\/td>\n\u7b49\u5ea6\u91cf\u6620\u5c04<\/td>\n<\/tr>\n
Jacobian Matrix<\/td>\n\u96c5\u53ef\u6bd4\u77e9\u9635<\/td>\n<\/tr>\n
Jensen Inequality<\/td>\nJensen\u4e0d\u7b49\u5f0f<\/td>\n<\/tr>\n
Joint Probability Distribution<\/td>\n\u8054\u5408\u6982\u7387\u5206\u5e03<\/td>\n<\/tr>\n
K-Armed Bandit Problem<\/td>\nk-\u6447\u81c2\u8001\u864e\u673a<\/td>\n<\/tr>\n
K-Fold Cross Validation<\/td>\nk \u6298\u4ea4\u53c9\u9a8c\u8bc1<\/td>\n<\/tr>\n
Karush-Kuhn-Tucker Condition<\/td>\nKKT\u6761\u4ef6<\/td>\n<\/tr>\n
Karush\u2013Kuhn\u2013Tucker<\/td>\nKarush\u2013Kuhn\u2013Tucker<\/td>\n<\/tr>\n
Kernel Function<\/td>\n\u6838\u51fd\u6570<\/td>\n<\/tr>\n
Kernel Method<\/td>\n\u6838\u65b9\u6cd5<\/td>\n<\/tr>\n
Kernel Trick<\/td>\n\u6838\u6280\u5de7<\/td>\n<\/tr>\n
Kernelized Linear Discriminant Analysis<\/td>\n\u6838\u7ebf\u6027\u5224\u522b\u5206\u6790<\/td>\n<\/tr>\n
KL Divergence<\/td>\nKL\u6563\u5ea6<\/td>\n<\/tr>\n
L-BFGS<\/td>\nL-BFGS<\/td>\n<\/tr>\n
Label<\/td>\n\u6807\u7b7e<\/td>\n<\/tr>\n
Label Space<\/td>\n\u6807\u8bb0\u7a7a\u95f4<\/td>\n<\/tr>\n
Lagrange Duality<\/td>\n\u62c9\u683c\u6717\u65e5\u5bf9\u5076\u6027<\/td>\n<\/tr>\n
Lagrange Multiplier<\/td>\n\u62c9\u683c\u6717\u65e5\u4e58\u5b50<\/td>\n<\/tr>\n
Language Model<\/td>\n\u8bed\u8a00\u6a21\u578b<\/td>\n<\/tr>\n
Laplace Smoothing<\/td>\n\u62c9\u666e\u62c9\u65af\u5e73\u6ed1<\/td>\n<\/tr>\n
Laplacian Correction<\/td>\n\u62c9\u666e\u62c9\u65af\u4fee\u6b63<\/td>\n<\/tr>\n
Latent Dirichlet Allocation<\/td>\n\u6f5c\u5728\u72c4\u5229\u514b\u96f7\u5206\u914d<\/td>\n<\/tr>\n
Latent Semantic Analysis<\/td>\n\u6f5c\u5728\u8bed\u4e49\u5206\u6790<\/td>\n<\/tr>\n
Latent Variable<\/td>\n\u6f5c\u53d8\u91cf\/\u9690\u53d8\u91cf<\/td>\n<\/tr>\n
Law of Large Numbers<\/td>\n\u5927\u6570\u5b9a\u5f8b<\/td>\n<\/tr>\n
Layer Normalization<\/td>\n\u5c42\u89c4\u8303\u5316<\/td>\n<\/tr>\n
Lazy Learning<\/td>\n\u61d2\u60f0\u5b66\u4e60<\/td>\n<\/tr>\n
Leaky Relu<\/td>\n\u6cc4\u6f0f\u4fee\u6b63\u7ebf\u6027\u5355\u5143\/\u6cc4\u6f0f\u6574\u6d41\u7ebf\u6027\u5355\u5143<\/td>\n<\/tr>\n
Learner<\/td>\n\u5b66\u4e60\u5668<\/td>\n<\/tr>\n
Learning<\/td>\n\u5b66\u4e60<\/td>\n<\/tr>\n
Learning By Analogy<\/td>\n\u7c7b\u6bd4\u5b66\u4e60<\/td>\n<\/tr>\n
Learning Rate<\/td>\n\u5b66\u4e60\u7387<\/td>\n<\/tr>\n
Learning Vector Quantization<\/td>\n\u5b66\u4e60\u5411\u91cf\u91cf\u5316<\/td>\n<\/tr>\n
Least Square Method<\/td>\n\u6700\u5c0f\u4e8c\u4e58\u6cd5<\/td>\n<\/tr>\n
Least Squares Regression Tree<\/td>\n\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\u6811<\/td>\n<\/tr>\n
Left Singular Vector<\/td>\n\u5de6\u5947\u5f02\u5411\u91cf<\/td>\n<\/tr>\n
Likelihood<\/td>\n\u4f3c\u7136<\/td>\n<\/tr>\n
Linear Chain Conditional Random Field<\/td>\n\u7ebf\u6027\u94fe\u6761\u4ef6\u968f\u673a\u573a<\/td>\n<\/tr>\n
Linear Classification Model<\/td>\n\u7ebf\u6027\u5206\u7c7b\u6a21\u578b<\/td>\n<\/tr>\n
Linear Classifier<\/td>\n\u7ebf\u6027\u5206\u7c7b\u5668<\/td>\n<\/tr>\n
Linear Dependence<\/td>\n\u7ebf\u6027\u76f8\u5173<\/td>\n<\/tr>\n
Linear Discriminant Analysis<\/td>\n\u7ebf\u6027\u5224\u522b\u5206\u6790<\/td>\n<\/tr>\n
Linear Model<\/td>\n\u7ebf\u6027\u6a21\u578b<\/td>\n<\/tr>\n
Linear Regression<\/td>\n\u7ebf\u6027\u56de\u5f52<\/td>\n<\/tr>\n
Link Function<\/td>\n\u8054\u7cfb\u51fd\u6570<\/td>\n<\/tr>\n
Local Markov Property<\/td>\n\u5c40\u90e8\u9a6c\u5c14\u53ef\u592b\u6027<\/td>\n<\/tr>\n
Local Minima<\/td>\n\u5c40\u90e8\u6781\u5c0f<\/td>\n<\/tr>\n
Local Minimum<\/td>\n\u5c40\u90e8\u6781\u5c0f<\/td>\n<\/tr>\n
Local Representation<\/td>\n\u5c40\u90e8\u5f0f\u8868\u793a\/\u5c40\u90e8\u5f0f\u8868\u5f81<\/td>\n<\/tr>\n
Log Likelihood<\/td>\n\u5bf9\u6570\u4f3c\u7136\u51fd\u6570<\/td>\n<\/tr>\n
Log Linear Model<\/td>\n\u5bf9\u6570\u7ebf\u6027\u6a21\u578b<\/td>\n<\/tr>\n
Log-Likelihood<\/td>\n\u5bf9\u6570\u4f3c\u7136<\/td>\n<\/tr>\n
Log-Linear Regression<\/td>\n\u5bf9\u6570\u7ebf\u6027\u56de\u5f52<\/td>\n<\/tr>\n
Logistic Function<\/td>\n\u5bf9\u6570\u51e0\u7387\u51fd\u6570<\/td>\n<\/tr>\n
Logistic Regression<\/td>\n\u5bf9\u6570\u51e0\u7387\u56de\u5f52<\/td>\n<\/tr>\n
Logit<\/td>\n\u5bf9\u6570\u51e0\u7387<\/td>\n<\/tr>\n
Long Short Term Memory<\/td>\n\u957f\u77ed\u671f\u8bb0\u5fc6<\/td>\n<\/tr>\n
Long Short-Term Memory Network<\/td>\n\u957f\u77ed\u671f\u8bb0\u5fc6\u7f51\u7edc<\/td>\n<\/tr>\n
Loopy Belief Propagation<\/td>\n\u73af\u72b6\u4fe1\u5ff5\u4f20\u64ad<\/td>\n<\/tr>\n
Loss Function<\/td>\n\u635f\u5931\u51fd\u6570<\/td>\n<\/tr>\n
Low Rank Matrix Approximation<\/td>\n\u4f4e\u79e9\u77e9\u9635\u8fd1\u4f3c<\/td>\n<\/tr>\n
Machine Learning<\/td>\n\u673a\u5668\u5b66\u4e60<\/td>\n<\/tr>\n
Macron-R<\/td>\n\u5b8f\u67e5\u5168\u7387<\/td>\n<\/tr>\n
Manhattan Distance<\/td>\n\u66fc\u54c8\u987f\u8ddd\u79bb<\/td>\n<\/tr>\n
Manifold<\/td>\n\u6d41\u5f62<\/td>\n<\/tr>\n
Manifold Assumption<\/td>\n\u6d41\u5f62\u5047\u8bbe<\/td>\n<\/tr>\n
Manifold Learning<\/td>\n\u6d41\u5f62\u5b66\u4e60<\/td>\n<\/tr>\n
Margin<\/td>\n\u95f4\u9694<\/td>\n<\/tr>\n
Marginal Distribution<\/td>\n\u8fb9\u7f18\u5206\u5e03<\/td>\n<\/tr>\n
Marginal Independence<\/td>\n\u8fb9\u7f18\u72ec\u7acb\u6027<\/td>\n<\/tr>\n
Marginalization<\/td>\n\u8fb9\u7f18\u5316<\/td>\n<\/tr>\n
Markov Chain<\/td>\n\u9a6c\u5c14\u53ef\u592b\u94fe<\/td>\n<\/tr>\n
Markov Chain Monte Carlo<\/td>\n\u9a6c\u5c14\u53ef\u592b\u94fe\u8499\u7279\u5361\u7f57<\/td>\n<\/tr>\n
Markov Decision Process<\/td>\n\u9a6c\u5c14\u53ef\u592b\u51b3\u7b56\u8fc7\u7a0b<\/td>\n<\/tr>\n
Markov Network<\/td>\n\u9a6c\u5c14\u53ef\u592b\u7f51\u7edc<\/td>\n<\/tr>\n
Markov Process<\/td>\n\u9a6c\u5c14\u53ef\u592b\u8fc7\u7a0b<\/td>\n<\/tr>\n
Markov Random Field<\/td>\n\u9a6c\u5c14\u53ef\u592b\u968f\u673a\u573a<\/td>\n<\/tr>\n
Mask<\/td>\n\u63a9\u7801<\/td>\n<\/tr>\n
Matrix<\/td>\n\u77e9\u9635<\/td>\n<\/tr>\n
Matrix Inversion<\/td>\n\u9006\u77e9\u9635<\/td>\n<\/tr>\n
Max Pooling<\/td>\n\u6700\u5927\u6c47\u805a<\/td>\n<\/tr>\n
Maximal Clique<\/td>\n\u6700\u5927\u56e2<\/td>\n<\/tr>\n
Maximum Entropy Model<\/td>\n\u6700\u5927\u71b5\u6a21\u578b<\/td>\n<\/tr>\n
Maximum Likelihood Estimation<\/td>\n\u6781\u5927\u4f3c\u7136\u4f30\u8ba1<\/td>\n<\/tr>\n
Maximum Margin<\/td>\n\u6700\u5927\u95f4\u9694<\/td>\n<\/tr>\n
Mean Filed<\/td>\n\u5e73\u5747\u573a<\/td>\n<\/tr>\n
Mean Pooling<\/td>\n\u5e73\u5747\u6c47\u805a<\/td>\n<\/tr>\n
Mean Squared Error<\/td>\n\u5747\u65b9\u8bef\u5dee<\/td>\n<\/tr>\n
Mean-Field<\/td>\n\u5e73\u5747\u573a<\/td>\n<\/tr>\n
Memory Network<\/td>\n\u8bb0\u5fc6\u7f51\u7edc<\/td>\n<\/tr>\n
Message Passing<\/td>\n\u6d88\u606f\u4f20\u9012<\/td>\n<\/tr>\n
Metric Learning<\/td>\n\u5ea6\u91cf\u5b66\u4e60<\/td>\n<\/tr>\n
Micro-R<\/td>\n\u5fae\u67e5\u5168\u7387<\/td>\n<\/tr>\n
Minibatch<\/td>\n\u5c0f\u6279\u91cf<\/td>\n<\/tr>\n
Minimal Description Length<\/td>\n\u6700\u5c0f\u63cf\u8ff0\u957f\u5ea6<\/td>\n<\/tr>\n
Minimax Game<\/td>\n\u6781\u5c0f\u6781\u5927\u535a\u5f08<\/td>\n<\/tr>\n
Minkowski Distance<\/td>\n\u95f5\u53ef\u592b\u65af\u57fa\u8ddd\u79bb<\/td>\n<\/tr>\n
Mixture of Experts<\/td>\n\u6df7\u5408\u4e13\u5bb6\u6a21\u578b<\/td>\n<\/tr>\n
Mixture-of-Gaussian<\/td>\n\u9ad8\u65af\u6df7\u5408<\/td>\n<\/tr>\n
Model<\/td>\n\u6a21\u578b<\/td>\n<\/tr>\n
Model Selection<\/td>\n\u6a21\u578b\u9009\u62e9<\/td>\n<\/tr>\n
Momentum Method<\/td>\n\u52a8\u91cf\u6cd5<\/td>\n<\/tr>\n
Monte Carlo Method<\/td>\n\u8499\u7279\u5361\u7f57\u65b9\u6cd5<\/td>\n<\/tr>\n
Moral Graph<\/td>\n\u7aef\u6b63\u56fe\/\u9053\u5fb7\u56fe<\/td>\n<\/tr>\n
Moralization<\/td>\n\u9053\u5fb7\u5316<\/td>\n<\/tr>\n
Multi-Class Classification<\/td>\n\u591a\u5206\u7c7b<\/td>\n<\/tr>\n
Multi-Head Attention<\/td>\n\u591a\u5934\u6ce8\u610f\u529b<\/td>\n<\/tr>\n
Multi-Head Self-Attention<\/td>\n\u591a\u5934\u81ea\u6ce8\u610f\u529b<\/td>\n<\/tr>\n
Multi-Kernel Learning<\/td>\n\u591a\u6838\u5b66\u4e60<\/td>\n<\/tr>\n
Multi-Label Learning<\/td>\n\u591a\u6807\u8bb0\u5b66\u4e60<\/td>\n<\/tr>\n
Multi-Layer Feedforward Neural Networks<\/td>\n\u591a\u5c42\u524d\u9988\u795e\u7ecf\u7f51\u7edc<\/td>\n<\/tr>\n
Multi-Layer Perceptron<\/td>\n\u591a\u5c42\u611f\u77e5\u673a<\/td>\n<\/tr>\n
Multinomial Distribution<\/td>\n\u591a\u9879\u5206\u5e03<\/td>\n<\/tr>\n
Multiple Dimensional Scaling<\/td>\n\u591a\u7ef4\u7f29\u653e<\/td>\n<\/tr>\n
Multiple Linear Regression<\/td>\n\u591a\u5143\u7ebf\u6027\u56de\u5f52<\/td>\n<\/tr>\n
Multitask Learning<\/td>\n\u591a\u4efb\u52a1\u5b66\u4e60<\/td>\n<\/tr>\n
Multivariate Normal Distribution<\/td>\n\u591a\u5143\u6b63\u6001\u5206\u5e03<\/td>\n<\/tr>\n
Mutual Information<\/td>\n\u4e92\u4fe1\u606f<\/td>\n<\/tr>\n
N-Gram Model<\/td>\nN\u5143\u6a21\u578b<\/td>\n<\/tr>\n
Naive Bayes Classifier<\/td>\n\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u5668<\/td>\n<\/tr>\n
Naive Bayes<\/td>\n\u6734\u7d20\u8d1d\u53f6\u65af<\/td>\n<\/tr>\n
Nearest Neighbor Classifier<\/td>\n\u6700\u8fd1\u90bb\u5206\u7c7b\u5668<\/td>\n<\/tr>\n
Negative Log Likelihood<\/td>\n\u8d1f\u5bf9\u6570\u4f3c\u7136\u51fd\u6570<\/td>\n<\/tr>\n
Neighbourhood Component Analysis<\/td>\n\u8fd1\u90bb\u6210\u5206\u5206\u6790<\/td>\n<\/tr>\n
Net Input<\/td>\n\u51c0\u8f93\u5165<\/td>\n<\/tr>\n
Neural Network<\/td>\n\u795e\u7ecf\u7f51\u7edc<\/td>\n<\/tr>\n
Neural Turing Machine<\/td>\n\u795e\u7ecf\u56fe\u7075\u673a<\/td>\n<\/tr>\n
Neuron<\/td>\n\u795e\u7ecf\u5143<\/td>\n<\/tr>\n
Newton Method<\/td>\n\u725b\u987f\u6cd5<\/td>\n<\/tr>\n
No Free Lunch Theorem<\/td>\n\u6ca1\u6709\u514d\u8d39\u5348\u9910\u5b9a\u7406<\/td>\n<\/tr>\n
Noise-Contrastive Estimation<\/td>\n\u566a\u58f0\u5bf9\u6bd4\u4f30\u8ba1<\/td>\n<\/tr>\n
Nominal Attribute<\/td>\n\u5217\u540d\u5c5e\u6027<\/td>\n<\/tr>\n
Non-Convex Optimization<\/td>\n\u975e\u51f8\u4f18\u5316<\/td>\n<\/tr>\n
Non-Metric Distance<\/td>\n\u975e\u5ea6\u91cf\u8ddd\u79bb<\/td>\n<\/tr>\n
Non-Negative Matrix Factorization<\/td>\n\u975e\u8d1f\u77e9\u9635\u5206\u89e3<\/td>\n<\/tr>\n
Non-Ordinal Attribute<\/td>\n\u65e0\u5e8f\u5c5e\u6027<\/td>\n<\/tr>\n
Norm<\/td>\n\u8303\u6570<\/td>\n<\/tr>\n
Normal Distribution<\/td>\n\u6b63\u6001\u5206\u5e03<\/td>\n<\/tr>\n
Normalization<\/td>\n\u89c4\u8303\u5316<\/td>\n<\/tr>\n
Nuclear Norm<\/td>\n\u6838\u8303\u6570<\/td>\n<\/tr>\n
Number of Epochs<\/td>\n\u8f6e\u6570<\/td>\n<\/tr>\n
Numerical Attribute<\/td>\n\u6570\u503c\u5c5e\u6027<\/td>\n<\/tr>\n
Object Detection<\/td>\n\u76ee\u6807\u68c0\u6d4b<\/td>\n<\/tr>\n
Oblique Decision Tree<\/td>\n\u659c\u51b3\u7b56\u6811<\/td>\n<\/tr>\n
Occam’s Razor<\/td>\n\u5965\u5361\u59c6\u5243\u5200<\/td>\n<\/tr>\n
Odds<\/td>\n\u51e0\u7387<\/td>\n<\/tr>\n
Off-Policy<\/td>\n\u5f02\u7b56\u7565<\/td>\n<\/tr>\n
On-Policy<\/td>\n\u540c\u7b56\u7565<\/td>\n<\/tr>\n
One-Dependent Estimator<\/td>\n\u72ec\u4f9d\u8d56\u4f30\u8ba1<\/td>\n<\/tr>\n
One-Hot<\/td>\n\u72ec\u70ed<\/td>\n<\/tr>\n
Online Learning<\/td>\n\u5728\u7ebf\u5b66\u4e60<\/td>\n<\/tr>\n
Optimizer<\/td>\n\u4f18\u5316\u5668<\/td>\n<\/tr>\n
Ordinal Attribute<\/td>\n\u6709\u5e8f\u5c5e\u6027<\/td>\n<\/tr>\n
Orthogonal<\/td>\n\u6b63\u4ea4<\/td>\n<\/tr>\n
Orthogonal Matrix<\/td>\n\u6b63\u4ea4\u77e9\u9635<\/td>\n<\/tr>\n
Out-Of-Bag Estimate<\/td>\n\u5305\u5916\u4f30\u8ba1<\/td>\n<\/tr>\n
Outlier<\/td>\n\u5f02\u5e38\u70b9<\/td>\n<\/tr>\n
Over-Parameterized<\/td>\n\u8fc7\u5ea6\u53c2\u6570\u5316<\/td>\n<\/tr>\n
Overfitting<\/td>\n\u8fc7\u62df\u5408<\/td>\n<\/tr>\n
Oversampling<\/td>\n\u8fc7\u91c7\u6837<\/td>\n<\/tr>\n
Pac-Learnable<\/td>\nPAC\u53ef\u5b66\u4e60<\/td>\n<\/tr>\n
Padding<\/td>\n\u586b\u5145<\/td>\n<\/tr>\n
Pairwise Markov Property<\/td>\n\u6210\u5bf9\u9a6c\u5c14\u53ef\u592b\u6027<\/td>\n<\/tr>\n
Parallel Distributed Processing<\/td>\n\u5206\u5e03\u5f0f\u5e76\u884c\u5904\u7406<\/td>\n<\/tr>\n
Parameter<\/td>\n\u53c2\u6570<\/td>\n<\/tr>\n
Parameter Estimation<\/td>\n\u53c2\u6570\u4f30\u8ba1<\/td>\n<\/tr>\n
Parameter Space<\/td>\n\u53c2\u6570\u7a7a\u95f4<\/td>\n<\/tr>\n
Parameter Tuning<\/td>\n\u8c03\u53c2<\/td>\n<\/tr>\n
Parametric ReLU<\/td>\n\u53c2\u6570\u5316\u4fee\u6b63\u7ebf\u6027\u5355\u5143\/\u53c2\u6570\u5316\u6574\u6d41\u7ebf\u6027\u5355\u5143<\/td>\n<\/tr>\n
Part-Of-Speech Tagging<\/td>\n\u8bcd\u6027\u6807\u6ce8<\/td>\n<\/tr>\n
Partial Derivative<\/td>\n\u504f\u5bfc\u6570<\/td>\n<\/tr>\n
Partially Observable Markov Decision Processes<\/td>\n\u90e8\u5206\u53ef\u89c2\u6d4b\u9a6c\u5c14\u53ef\u592b\u51b3\u7b56\u8fc7\u7a0b<\/td>\n<\/tr>\n
Partition Function<\/td>\n\u914d\u5206\u51fd\u6570<\/td>\n<\/tr>\n
Perceptron<\/td>\n\u611f\u77e5\u673a<\/td>\n<\/tr>\n
Performance Measure<\/td>\n\u6027\u80fd\u5ea6\u91cf<\/td>\n<\/tr>\n
Perplexity<\/td>\n\u56f0\u60d1\u5ea6<\/td>\n<\/tr>\n
Pointer Network<\/td>\n\u6307\u9488\u7f51\u7edc<\/td>\n<\/tr>\n
Policy<\/td>\n\u7b56\u7565<\/td>\n<\/tr>\n
Policy Gradient<\/td>\n\u7b56\u7565\u68af\u5ea6<\/td>\n<\/tr>\n
Policy Iteration<\/td>\n\u7b56\u7565\u8fed\u4ee3<\/td>\n<\/tr>\n
Polynomial Kernel Function<\/td>\n\u591a\u9879\u5f0f\u6838\u51fd\u6570<\/td>\n<\/tr>\n
Pooling<\/td>\n\u6c47\u805a<\/td>\n<\/tr>\n
Pooling Layer<\/td>\n\u6c47\u805a\u5c42<\/td>\n<\/tr>\n
Positive Definite Matrix<\/td>\n\u6b63\u5b9a\u77e9\u9635<\/td>\n<\/tr>\n
Post-Pruning<\/td>\n\u540e\u526a\u679d<\/td>\n<\/tr>\n
Potential Function<\/td>\n\u52bf\u51fd\u6570<\/td>\n<\/tr>\n
Power Method<\/td>\n\u5e42\u6cd5<\/td>\n<\/tr>\n
Pre-Training<\/td>\n\u9884\u8bad\u7ec3<\/td>\n<\/tr>\n
Precision<\/td>\n\u67e5\u51c6\u7387\/\u51c6\u786e\u7387<\/td>\n<\/tr>\n
Prepruning<\/td>\n\u9884\u526a\u679d<\/td>\n<\/tr>\n
Primal Problem<\/td>\n\u4e3b\u95ee\u9898<\/td>\n<\/tr>\n
Primary Visual Cortex<\/td>\n\u521d\u7ea7\u89c6\u89c9\u76ae\u5c42<\/td>\n<\/tr>\n
Principal Component Analysis<\/td>\n\u4e3b\u6210\u5206\u5206\u6790<\/td>\n<\/tr>\n
Prior<\/td>\n\u5148\u9a8c<\/td>\n<\/tr>\n
Probabilistic Context-Free Grammar<\/td>\n\u6982\u7387\u4e0a\u4e0b\u6587\u65e0\u5173\u6587\u6cd5<\/td>\n<\/tr>\n
Probabilistic Graphical Model<\/td>\n\u6982\u7387\u56fe\u6a21\u578b<\/td>\n<\/tr>\n
Probabilistic Model<\/td>\n\u6982\u7387\u6a21\u578b<\/td>\n<\/tr>\n
Probability Density Function<\/td>\n\u6982\u7387\u5bc6\u5ea6\u51fd\u6570<\/td>\n<\/tr>\n
Probability Distribution<\/td>\n\u6982\u7387\u5206\u5e03<\/td>\n<\/tr>\n
Probably Approximately Correct<\/td>\n\u6982\u7387\u8fd1\u4f3c\u6b63\u786e<\/td>\n<\/tr>\n
Proposal Distribution<\/td>\n\u63d0\u8bae\u5206\u5e03<\/td>\n<\/tr>\n
Prototype-Based Clustering<\/td>\n\u539f\u578b\u805a\u7c7b<\/td>\n<\/tr>\n
Proximal Gradient Descent<\/td>\n\u8fd1\u7aef\u68af\u5ea6\u4e0b\u964d<\/td>\n<\/tr>\n
Pruning<\/td>\n\u526a\u679d<\/td>\n<\/tr>\n
Quadratic Loss Function<\/td>\n\u5e73\u65b9\u635f\u5931\u51fd\u6570<\/td>\n<\/tr>\n
Quadratic Programming<\/td>\n\u4e8c\u6b21\u89c4\u5212<\/td>\n<\/tr>\n
Quasi Newton Method<\/td>\n\u62df\u725b\u987f\u6cd5<\/td>\n<\/tr>\n
Radial Basis Function<\/td>\n\u5f84\u5411\u57fa\u51fd\u6570<\/td>\n<\/tr>\n
Random Forest<\/td>\n\u968f\u673a\u68ee\u6797<\/td>\n<\/tr>\n
Random Sampling<\/td>\n\u968f\u673a\u91c7\u6837<\/td>\n<\/tr>\n
Random Search<\/td>\n\u968f\u673a\u641c\u7d22<\/td>\n<\/tr>\n
Random Variable<\/td>\n\u968f\u673a\u53d8\u91cf<\/td>\n<\/tr>\n
Random Walk<\/td>\n\u968f\u673a\u6e38\u8d70<\/td>\n<\/tr>\n
Recall<\/td>\n\u67e5\u5168\u7387\/\u53ec\u56de\u7387<\/td>\n<\/tr>\n
Receptive Field<\/td>\n\u611f\u53d7\u91ce<\/td>\n<\/tr>\n
Reconstruction Error<\/td>\n\u91cd\u6784\u8bef\u5dee<\/td>\n<\/tr>\n
Rectified Linear Unit<\/td>\n\u4fee\u6b63\u7ebf\u6027\u5355\u5143\/\u6574\u6d41\u7ebf\u6027\u5355\u5143<\/td>\n<\/tr>\n
Recurrent Neural Network<\/td>\n\u5faa\u73af\u795e\u7ecf\u7f51\u7edc<\/td>\n<\/tr>\n
Recursive Neural Network<\/td>\n\u9012\u5f52\u795e\u7ecf\u7f51\u7edc<\/td>\n<\/tr>\n
Regression<\/td>\n\u56de\u5f52<\/td>\n<\/tr>\n
Regularization<\/td>\n\u6b63\u5219\u5316<\/td>\n<\/tr>\n
Regularizer<\/td>\n\u6b63\u5219\u5316\u9879<\/td>\n<\/tr>\n
Reinforcement Learning<\/td>\n\u5f3a\u5316\u5b66\u4e60<\/td>\n<\/tr>\n
Relative Entropy<\/td>\n\u76f8\u5bf9\u71b5<\/td>\n<\/tr>\n
Reparameterization<\/td>\n\u518d\u53c2\u6570\u5316\/\u91cd\u53c2\u6570\u5316<\/td>\n<\/tr>\n
Representation<\/td>\n\u8868\u793a<\/td>\n<\/tr>\n
Representation Learning<\/td>\n\u8868\u793a\u5b66\u4e60<\/td>\n<\/tr>\n
Representer Theorem<\/td>\n\u8868\u793a\u5b9a\u7406<\/td>\n<\/tr>\n
Reproducing Kernel Hilbert Space<\/td>\n\u518d\u751f\u6838\u5e0c\u5c14\u4f2f\u7279\u7a7a\u95f4<\/td>\n<\/tr>\n
Rescaling<\/td>\n\u518d\u7f29\u653e<\/td>\n<\/tr>\n
Reset Gate<\/td>\n\u91cd\u7f6e\u95e8<\/td>\n<\/tr>\n
Residual Connection<\/td>\n\u6b8b\u5dee\u8fde\u63a5<\/td>\n<\/tr>\n
Residual Network<\/td>\n\u6b8b\u5dee\u7f51\u7edc<\/td>\n<\/tr>\n
Restricted Boltzmann Machine<\/td>\n\u53d7\u9650\u73bb\u5c14\u5179\u66fc\u673a<\/td>\n<\/tr>\n
Reward<\/td>\n\u5956\u52b1<\/td>\n<\/tr>\n
Ridge Regression<\/td>\n\u5cad\u56de\u5f52<\/td>\n<\/tr>\n
Right Singular Vector<\/td>\n\u53f3\u5947\u5f02\u5411\u91cf<\/td>\n<\/tr>\n
Risk<\/td>\n\u98ce\u9669<\/td>\n<\/tr>\n
Robustness<\/td>\n\u7a33\u5065\u6027<\/td>\n<\/tr>\n
Root Node<\/td>\n\u6839\u7ed3\u70b9<\/td>\n<\/tr>\n
Rule Learning<\/td>\n\u89c4\u5219\u5b66\u4e60<\/td>\n<\/tr>\n
Saddle Point<\/td>\n\u978d\u70b9<\/td>\n<\/tr>\n
Sample<\/td>\n\u6837\u672c<\/td>\n<\/tr>\n
Sample Complexity<\/td>\n\u6837\u672c\u590d\u6742\u5ea6<\/td>\n<\/tr>\n
Sample Space<\/td>\n\u6837\u672c\u7a7a\u95f4<\/td>\n<\/tr>\n
Scalar<\/td>\n\u6807\u91cf<\/td>\n<\/tr>\n
Selective Ensemble<\/td>\n\u9009\u62e9\u6027\u96c6\u6210<\/td>\n<\/tr>\n
Self Information<\/td>\n\u81ea\u4fe1\u606f<\/td>\n<\/tr>\n
Self-Attention<\/td>\n\u81ea\u6ce8\u610f\u529b<\/td>\n<\/tr>\n
Self-Organizing Map<\/td>\n\u81ea\u7ec4\u7ec7\u6620\u5c04\u7f51<\/td>\n<\/tr>\n
Self-Training<\/td>\n\u81ea\u8bad\u7ec3<\/td>\n<\/tr>\n
Semi-Definite Programming<\/td>\n\u534a\u6b63\u5b9a\u89c4\u5212<\/td>\n<\/tr>\n
Semi-Naive Bayes Classifiers<\/td>\n\u534a\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u5668<\/td>\n<\/tr>\n
Semi-Restricted Boltzmann Machine<\/td>\n\u534a\u53d7\u9650\u73bb\u5c14\u5179\u66fc\u673a<\/td>\n<\/tr>\n
Semi-Supervised Clustering<\/td>\n\u534a\u76d1\u7763\u805a\u7c7b<\/td>\n<\/tr>\n
Semi-Supervised Learning<\/td>\n\u534a\u76d1\u7763\u5b66\u4e60<\/td>\n<\/tr>\n
Semi-Supervised Support Vector Machine<\/td>\n\u534a\u76d1\u7763\u652f\u6301\u5411\u91cf\u673a<\/td>\n<\/tr>\n
Sentiment Analysis<\/td>\n\u60c5\u611f\u5206\u6790<\/td>\n<\/tr>\n
Separating Hyperplane<\/td>\n\u5206\u79bb\u8d85\u5e73\u9762<\/td>\n<\/tr>\n
Sequential Covering<\/td>\n\u5e8f\u8d2f\u8986\u76d6<\/td>\n<\/tr>\n
Sigmoid Belief Network<\/td>\nSigmoid\u4fe1\u5ff5\u7f51\u7edc<\/td>\n<\/tr>\n
Sigmoid Function<\/td>\nSigmoid\u51fd\u6570<\/td>\n<\/tr>\n
Signed Distance<\/td>\n\u5e26\u7b26\u53f7\u8ddd\u79bb<\/td>\n<\/tr>\n
Similarity Measure<\/td>\n\u76f8\u4f3c\u5ea6\u5ea6\u91cf<\/td>\n<\/tr>\n
Simulated Annealing<\/td>\n\u6a21\u62df\u9000\u706b<\/td>\n<\/tr>\n
Simultaneous Localization And Mapping<\/td>\n\u5373\u65f6\u5b9a\u4f4d\u4e0e\u5730\u56fe\u6784\u5efa<\/td>\n<\/tr>\n
Singular Value<\/td>\n\u5947\u5f02\u503c<\/td>\n<\/tr>\n
Singular Value Decomposition<\/td>\n\u5947\u5f02\u503c\u5206\u89e3<\/td>\n<\/tr>\n
Skip-Gram Model<\/td>\n\u8df3\u5143\u6a21\u578b<\/td>\n<\/tr>\n
Smoothing<\/td>\n\u5e73\u6ed1<\/td>\n<\/tr>\n
Soft Margin<\/td>\n\u8f6f\u95f4\u9694<\/td>\n<\/tr>\n
Soft Margin Maximization<\/td>\n\u8f6f\u95f4\u9694\u6700\u5927\u5316<\/td>\n<\/tr>\n
Softmax<\/td>\nSoftmax\/\u8f6f\u6700\u5927\u5316<\/td>\n<\/tr>\n
Softmax Function<\/td>\nSoftmax\u51fd\u6570\/\u8f6f\u6700\u5927\u5316\u51fd\u6570<\/td>\n<\/tr>\n
Softmax Regression<\/td>\nSoftmax\u56de\u5f52\/\u8f6f\u6700\u5927\u5316\u56de\u5f52<\/td>\n<\/tr>\n
Softplus Function<\/td>\nSoftplus\u51fd\u6570<\/td>\n<\/tr>\n
Span<\/td>\n\u5f20\u6210\u5b50\u7a7a\u95f4<\/td>\n<\/tr>\n
Sparse Coding<\/td>\n\u7a00\u758f\u7f16\u7801<\/td>\n<\/tr>\n
Sparse Representation<\/td>\n\u7a00\u758f\u8868\u793a<\/td>\n<\/tr>\n
Sparsity<\/td>\n\u7a00\u758f\u6027<\/td>\n<\/tr>\n
Specialization<\/td>\n\u7279\u5316<\/td>\n<\/tr>\n
Splitting Variable<\/td>\n\u5207\u5206\u53d8\u91cf<\/td>\n<\/tr>\n
Squashing Function<\/td>\n\u6324\u538b\u51fd\u6570<\/td>\n<\/tr>\n
Standard Normal Distribution<\/td>\n\u6807\u51c6\u6b63\u6001\u5206\u5e03<\/td>\n<\/tr>\n
State<\/td>\n\u72b6\u6001<\/td>\n<\/tr>\n
State Value Function<\/td>\n\u72b6\u6001\u503c\u51fd\u6570<\/td>\n<\/tr>\n
State-Action Value Function<\/td>\n\u72b6\u6001-\u52a8\u4f5c\u503c\u51fd\u6570<\/td>\n<\/tr>\n
Stationary Distribution<\/td>\n\u5e73\u7a33\u5206\u5e03<\/td>\n<\/tr>\n
Stationary Point<\/td>\n\u9a7b\u70b9<\/td>\n<\/tr>\n
Statistical Learning<\/td>\n\u7edf\u8ba1\u5b66\u4e60<\/td>\n<\/tr>\n
Steepest Descent<\/td>\n\u6700\u901f\u4e0b\u964d\u6cd5<\/td>\n<\/tr>\n
Stochastic Gradient Descent<\/td>\n\u968f\u673a\u68af\u5ea6\u4e0b\u964d<\/td>\n<\/tr>\n
Stochastic Matrix<\/td>\n\u968f\u673a\u77e9\u9635<\/td>\n<\/tr>\n
Stochastic Process<\/td>\n\u968f\u673a\u8fc7\u7a0b<\/td>\n<\/tr>\n
Stratified Sampling<\/td>\n\u5206\u5c42\u91c7\u6837<\/td>\n<\/tr>\n
Stride<\/td>\n\u6b65\u5e45<\/td>\n<\/tr>\n
Structural Risk<\/td>\n\u7ed3\u6784\u98ce\u9669<\/td>\n<\/tr>\n
Structural Risk Minimization<\/td>\n\u7ed3\u6784\u98ce\u9669\u6700\u5c0f\u5316<\/td>\n<\/tr>\n
Subsample<\/td>\n\u5b50\u91c7\u6837<\/td>\n<\/tr>\n
Subsampling<\/td>\n\u4e0b\u91c7\u6837<\/td>\n<\/tr>\n
Subset Search<\/td>\n\u5b50\u96c6\u641c\u7d22<\/td>\n<\/tr>\n
Subspace<\/td>\n\u5b50\u7a7a\u95f4<\/td>\n<\/tr>\n
Supervised Learning<\/td>\n\u76d1\u7763\u5b66\u4e60<\/td>\n<\/tr>\n
Support Vector<\/td>\n\u652f\u6301\u5411\u91cf<\/td>\n<\/tr>\n
Support Vector Expansion<\/td>\n\u652f\u6301\u5411\u91cf\u5c55\u5f0f<\/td>\n<\/tr>\n
Support Vector Machine<\/td>\n\u652f\u6301\u5411\u91cf\u673a<\/td>\n<\/tr>\n
Surrogat Loss<\/td>\n\u66ff\u4ee3\u635f\u5931<\/td>\n<\/tr>\n
Surrogate Function<\/td>\n\u66ff\u4ee3\u51fd\u6570<\/td>\n<\/tr>\n
Surrogate Loss Function<\/td>\n\u4ee3\u7406\u635f\u5931\u51fd\u6570<\/td>\n<\/tr>\n
Symbolism<\/td>\n\u7b26\u53f7\u4e3b\u4e49<\/td>\n<\/tr>\n
Tangent Propagation<\/td>\n\u6b63\u5207\u4f20\u64ad<\/td>\n<\/tr>\n
Teacher Forcing<\/td>\n\u5f3a\u5236\u6559\u5b66<\/td>\n<\/tr>\n
Temporal-Difference Learning<\/td>\n\u65f6\u5e8f\u5dee\u5206\u5b66\u4e60<\/td>\n<\/tr>\n
Tensor<\/td>\n\u5f20\u91cf<\/td>\n<\/tr>\n
Test Error<\/td>\n\u6d4b\u8bd5\u8bef\u5dee<\/td>\n<\/tr>\n
Test Sample<\/td>\n\u6d4b\u8bd5\u6837\u672c<\/td>\n<\/tr>\n
Test Set<\/td>\n\u6d4b\u8bd5\u96c6<\/td>\n<\/tr>\n
Threshold<\/td>\n\u9608\u503c<\/td>\n<\/tr>\n
Threshold Logic Unit<\/td>\n\u9608\u503c\u903b\u8f91\u5355\u5143<\/td>\n<\/tr>\n
Threshold-Moving<\/td>\n\u9608\u503c\u79fb\u52a8<\/td>\n<\/tr>\n
Tied Weight<\/td>\n\u6346\u7ed1\u6743\u91cd<\/td>\n<\/tr>\n
Tikhonov Regularization<\/td>\nTikhonov\u6b63\u5219\u5316<\/td>\n<\/tr>\n
Time Delay Neural Network<\/td>\n\u65f6\u5ef6\u795e\u7ecf\u7f51\u7edc<\/td>\n<\/tr>\n
Time Homogenous Markov Chain<\/td>\n\u65f6\u95f4\u9f50\u6b21\u9a6c\u5c14\u53ef\u592b\u94fe<\/td>\n<\/tr>\n
Time Step<\/td>\n\u65f6\u95f4\u6b65<\/td>\n<\/tr>\n
Token<\/td>\n\u8bcd\u5143<\/td>\n<\/tr>\n
Token<\/td>\n\u8bcd\u5143<\/td>\n<\/tr>\n
Tokenization<\/td>\n\u8bcd\u5143\u5316<\/td>\n<\/tr>\n
Tokenizer<\/td>\n\u8bcd\u5143\u5206\u6790\u5668<\/td>\n<\/tr>\n
Topic Model<\/td>\n\u8bdd\u9898\u6a21\u578b<\/td>\n<\/tr>\n
Topic Modeling<\/td>\n\u8bdd\u9898\u5206\u6790<\/td>\n<\/tr>\n
Trace<\/td>\n\u8ff9<\/td>\n<\/tr>\n
Training<\/td>\n\u8bad\u7ec3<\/td>\n<\/tr>\n
Training Error<\/td>\n\u8bad\u7ec3\u8bef\u5dee<\/td>\n<\/tr>\n
Training Sample<\/td>\n\u8bad\u7ec3\u6837\u672c<\/td>\n<\/tr>\n
Training Set<\/td>\n\u8bad\u7ec3\u96c6<\/td>\n<\/tr>\n
Transductive Learning<\/td>\n\u76f4\u63a8\u5b66\u4e60<\/td>\n<\/tr>\n
Transductive Transfer Learning<\/td>\n\u76f4\u63a8\u8fc1\u79fb\u5b66\u4e60<\/td>\n<\/tr>\n
Transfer Learning<\/td>\n\u8fc1\u79fb\u5b66\u4e60<\/td>\n<\/tr>\n
Transformer<\/td>\nTransformer<\/td>\n<\/tr>\n
Transformer Model<\/td>\nTransformer\u6a21\u578b<\/td>\n<\/tr>\n
Transpose<\/td>\n\u8f6c\u7f6e<\/td>\n<\/tr>\n
Transposed Convolution<\/td>\n\u8f6c\u7f6e\u5377\u79ef<\/td>\n<\/tr>\n
Trial And Error<\/td>\n\u8bd5\u9519<\/td>\n<\/tr>\n
Trigram<\/td>\n\u4e09\u5143\u8bed\u6cd5<\/td>\n<\/tr>\n
Turing Machine<\/td>\n\u56fe\u7075\u673a<\/td>\n<\/tr>\n
Underfitting<\/td>\n\u6b20\u62df\u5408<\/td>\n<\/tr>\n
Undersampling<\/td>\n\u6b20\u91c7\u6837<\/td>\n<\/tr>\n
Undirected Graphical Model<\/td>\n\u65e0\u5411\u56fe\u6a21\u578b<\/td>\n<\/tr>\n
Uniform Distribution<\/td>\n\u5747\u5300\u5206\u5e03<\/td>\n<\/tr>\n
Unigram<\/td>\n\u4e00\u5143\u8bed\u6cd5<\/td>\n<\/tr>\n
Unit<\/td>\n\u5355\u5143<\/td>\n<\/tr>\n
Universal Approximation Theorem<\/td>\n\u901a\u7528\u8fd1\u4f3c\u5b9a\u7406<\/td>\n<\/tr>\n
Universal Approximator<\/td>\n\u901a\u7528\u8fd1\u4f3c\u5668<\/td>\n<\/tr>\n
Universal Function Approximator<\/td>\n\u901a\u7528\u51fd\u6570\u8fd1\u4f3c\u5668<\/td>\n<\/tr>\n
Unknown Token<\/td>\n\u672a\u77e5\u8bcd\u5143<\/td>\n<\/tr>\n
Unsupervised Layer-Wise Training<\/td>\n\u65e0\u76d1\u7763\u9010\u5c42\u8bad\u7ec3<\/td>\n<\/tr>\n
Unsupervised Learning<\/td>\n\u65e0\u76d1\u7763\u5b66\u4e60<\/td>\n<\/tr>\n
Update Gate<\/td>\n\u66f4\u65b0\u95e8<\/td>\n<\/tr>\n
Upsampling<\/td>\n\u4e0a\u91c7\u6837<\/td>\n<\/tr>\n
V-Structure<\/td>\nV\u578b\u7ed3\u6784<\/td>\n<\/tr>\n
Validation Set<\/td>\n\u9a8c\u8bc1\u96c6<\/td>\n<\/tr>\n
Validity Index<\/td>\n\u6709\u6548\u6027\u6307\u6807<\/td>\n<\/tr>\n
Value Function Approximation<\/td>\n\u503c\u51fd\u6570\u8fd1\u4f3c<\/td>\n<\/tr>\n
Value Iteration<\/td>\n\u503c\u8fed\u4ee3<\/td>\n<\/tr>\n
Vanishing Gradient Problem<\/td>\n\u68af\u5ea6\u6d88\u5931\u95ee\u9898<\/td>\n<\/tr>\n
Vapnik-Chervonenkis Dimension<\/td>\nVC\u7ef4<\/td>\n<\/tr>\n
Variable Elimination<\/td>\n\u53d8\u91cf\u6d88\u53bb<\/td>\n<\/tr>\n
Variance<\/td>\n\u65b9\u5dee<\/td>\n<\/tr>\n
Variational Autoencoder<\/td>\n\u53d8\u5206\u81ea\u7f16\u7801\u5668<\/td>\n<\/tr>\n
Variational Inference<\/td>\n\u53d8\u5206\u63a8\u65ad<\/td>\n<\/tr>\n
Vector<\/td>\n\u5411\u91cf<\/td>\n<\/tr>\n
Vector Space Model<\/td>\n\u5411\u91cf\u7a7a\u95f4\u6a21\u578b<\/td>\n<\/tr>\n
Version Space<\/td>\n\u7248\u672c\u7a7a\u95f4<\/td>\n<\/tr>\n
Viterbi Algorithm<\/td>\n\u7ef4\u7279\u6bd4\u7b97\u6cd5<\/td>\n<\/tr>\n
Vocabulary<\/td>\n\u8bcd\u8868<\/td>\n<\/tr>\n
Warp<\/td>\n\u7ebf\u7a0b\u675f<\/td>\n<\/tr>\n
Weak Learner<\/td>\n\u5f31\u5b66\u4e60\u5668<\/td>\n<\/tr>\n
Weakly Supervised Learning<\/td>\n\u5f31\u76d1\u7763\u5b66\u4e60<\/td>\n<\/tr>\n
Weight<\/td>\n\u6743\u91cd<\/td>\n<\/tr>\n
Weight Decay<\/td>\n\u6743\u91cd\u8870\u51cf<\/td>\n<\/tr>\n
Weight Sharing<\/td>\n\u6743\u5171\u4eab<\/td>\n<\/tr>\n
Weighted Voting<\/td>\n\u52a0\u6743\u6295\u7968<\/td>\n<\/tr>\n
Whitening<\/td>\n\u767d\u5316<\/td>\n<\/tr>\n
Winner-Take-All<\/td>\n\u80dc\u8005\u901a\u5403<\/td>\n<\/tr>\n
Within-Class Scatter Matrix<\/td>\n\u7c7b\u5185\u6563\u5ea6\u77e9\u9635<\/td>\n<\/tr>\n
Word Embedding<\/td>\n\u8bcd\u5d4c\u5165<\/td>\n<\/tr>\n
Word Sense Disambiguation<\/td>\n\u8bcd\u4e49\u6d88\u6b67<\/td>\n<\/tr>\n
Word Vector<\/td>\n\u8bcd\u5411\u91cf<\/td>\n<\/tr>\n
Zero Padding<\/td>\n\u96f6\u586b\u5145<\/td>\n<\/tr>\n
Zero-Shot Learning<\/td>\n\u96f6\u8bd5\u5b66\u4e60<\/td>\n<\/tr>\n
Zipf’s Law<\/td>\n\u9f50\u666e\u592b\u5b9a\u5f8b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/section>\n<\/section>\n
\n
\u7248\u6743\u58f0\u660e<\/strong><\/p>\n<\/section>\n<\/section>\n
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\u4f60\u597d\uff0c\u6211\u662fzhenguo \u8fd9\u662f\u51e0\u4f4d\u673a\u5668\u5b66\u4e60\u6743\u5a01\u4e13\u5bb6\u6c47\u603b\u7684725\u4e2a\u673a\u5668\u5b66\u4e60\u672f\u8bed\u8868\uff0c\u975e\u5e38\u5168\u9762\u4e86\uff0c\u503c\u5f97\u6536\u85cf\uff01 \u82f1\u6587\u672f […]<\/p>\n

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