Sources

Every explanatory claim on this site is attributed, and the build refuses to publish a page that breaks the rules below. This page exists so you can check that for yourself rather than take our word for it.

The build fails if

• a citation points at a source that was never declared
• a declared source is never actually cited — no decorative bibliographies
• a source sits on a host that is not on the list below
• a non-draft page carries fewer than two sources
• a documentation source has no retrieval date, because docs pages change

What counts as a source

What does not

Listed explicitly so the rule is never a judgement call.

• medium.comAnyone can publish; no review, no stable authorship.
• towardsdatascience.comMedium publication — same problem, plus frequent factual errors.
• geeksforgeeks.orgContent farm; known to paraphrase textbooks inaccurately.
• w3schools.comTutorial site, not a specification or reference.
• tutorialspoint.comContent farm.
• javatpoint.comContent farm.
• stackoverflow.comFine as a 'see also', never as the authority for a claim.
• quora.comUnreviewed user answers.
• wikipedia.orgUse it to FIND the primary source, then cite that source directly.
• chat.openai.comLanguage model output is not a source.
• claude.aiLanguage model output is not a source.

Sources in use (224)

Deduplicated across every page, with what cites them.

1. A Call for Clarity in Reporting BLEU Scores — Post (2018)paperCited by: BLEU, Text metrics
2. A Random Forest Guided Tour — Biau, Scornet (2015)paperCited by: Feature importance, Random forest
3. A Simple Framework for Contrastive Learning of Visual Representations — Chen, Kornblith, Norouzi, Hinton (2020)paperCited by: Self-supervised learning
4. A systematic study of the class imbalance problem in convolutional neural networks — Buda, Maki, Mazurowski (2018)paperCited by: Class imbalance
5. A Tutorial on Principal Component Analysis — Shlens (2014)paperCited by: Eigenvectors and eigenvalues, PCA
6. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour — Goyal, Dollár, Girshick, Noordhuis, Wesolowski, Kyrola, Tulloch, Jia, He (2017)paperCited by: Mini-batch gradient descent
7. Adam: A Method for Stochastic Optimization — Kingma, Ba (2014)paperCited by: Adam
8. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations — Lan, Chen, Goodman, Gimpel, Sharma, Soricut (2019)paperCited by: Transformer family
9. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale — Dosovitskiy, Beyer, Kolesnikov, Weissenborn, Zhai, Unterthiner, et al. (2021)paperCited by: CLIP, Vision transformer, Transformer family
10. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale — Dosovitskiy et al. (2020)paperCited by: Computer vision architectures
11. Are we done with ImageNet? — Beyer, Hénaff, Kolesnikov, Zhai, van den Oord (2020)paperCited by: Image classification
12. Attention Is All You Need — Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin (2017)paperCited by: Attention, Natural language processing, Transformer
13. Attention is not Explanation — Jain, Wallace (2019)paperCited by: Attention
14. Automatic Differentiation in Machine Learning: a Survey — Baydin, Pearlmutter, Radul, Siskind (2018)paperCited by: Backpropagation
15. average_precision_score — scikit-learn developersdocsCited by: Mean average precision
16. Baselines and Bigrams: Simple, Good Sentiment and Topic Classification — Wang, Manning (2012)paperCited by: Bag of words
17. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift — Ioffe, Szegedy (2015)paperCited by: Batch normalization
18. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding — Devlin, Chang, Lee, Toutanova (2019)paperCited by: BERT, Named entity recognition, Natural language processing, Transformer family
19. BERTScore: Evaluating Text Generation with BERT — Zhang, Kishore, Wu, Weinberger, Artzi (2020)paperCited by: BLEU, Text metrics
20. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models — Wei, Wang, Schuurmans, Bosma, Ichter, Xia, Chi, Le, Zhou (2022)paperCited by: Prompting
21. Computational reproducibility of Jupyter notebooks from biomedical publications — Samuel, Mietchen (2023)paperCited by: Jupyter Notebook
22. Cross-validation: what does it estimate and how well does it do it? — Bates, Hastie, Tibshirani (2023)paperCited by: Cross-validation, Validation strategies
23. Cyclical Learning Rates for Training Neural Networks — Leslie N. Smith (2017)paperCited by: Learning rate
24. Deconvolution and Checkerboard Artifacts — Odena, Dumoulin, Olah (2016)paperCited by: Transposed convolution
25. Decoupled Weight Decay Regularization — Loshchilov, Hutter (2019)paperCited by: Adam
26. Deep Learning — Goodfellow, Bengio, Courville (2016)bookCited by: Adam, BFGS, Conjugate gradient, Mini-batch gradient descent, SGD
27. Deep Learning — Goodfellow, Bengio, Courville (2016)bookCited by: Bagging, Dropout, Early stopping, Elastic Net, Lasso regression, Regularization, Ridge regression, Linear model family
28. Deep Learning — Goodfellow, Bengio, Courville (2016)bookCited by: Bayesian linear regression, Data augmentation, Eigenvectors and eigenvalues, Gradient, Gradient descent, Learning rate, Linear regression, Logistic regression, MAE, MSLE, Perceptron, Polynomial regression, ReLU, Sigmoid, Softmax, Tanh, Activation functions, Computer vision architectures, Optimizers
29. Deep Learning — Goodfellow, Bengio, CourvillebookCited by: Convolutional neural network
30. Deep Learning — Goodfellow, Bengio, Courville (2016)bookCited by: Decision tree, KNN, SVM
31. Deep Learning — Goodfellow, Bengio, Courville (2016)bookCited by: Leaky ReLU, Regression metrics
32. Deep Learning — Sequence Modeling, chapter 10 — Goodfellow, Bengio, CourvillebookCited by: Recurrent neural network
33. Deep Residual Learning for Image Recognition — He, Zhang, Ren, Sun (2015)paperCited by: Batch normalization, Convolutional neural network, Transfer learning, Computer vision architectures
34. Deep Residual Learning for Image Recognition — He, Zhang, Ren, Sun (2015)paperCited by: Residual connection
35. Deep Sets — Zaheer, Kottur, Ravanbakhsh, Poczos, Salakhutdinov, Smola (2017)paperCited by: PointNet
36. Deep Sparse Rectifier Neural Networks — Glorot, Bordes, Bengio (2011)paperCited by: ReLU
37. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification — He, Zhang, Ren, Sun (2015)paperCited by: Leaky ReLU
38. Demystifying MMD GANs — Bińkowski, Sutherland, Arbel, Gretton (2018)paperCited by: Fréchet Inception Distance
39. Denoising Diffusion Probabilistic Models — Ho, Jain, Abbeel (2020)paperCited by: Diffusion model, Fréchet Inception Distance, Generative adversarial network, Vision tasks
40. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter — Sanh, Debut, Chaumond, Wolf (2019)paperCited by: Transformer family
41. Dive into Deep Learning — Zhang, Lipton, Li, SmolabookCited by: Adam, Attention, BERT, Convolution, Convolutional neural network, Data augmentation, Feature engineering, Gradient, Gradient descent, Leaky ReLU, Mini-batch gradient descent, Natural language processing, Overfitting, Pooling, Receptive field, ReLU, SGD, Sigmoid, Tanh, Tokenization, Transfer learning, Transformer, Word embeddings, Activation functions, Optimizers
42. Dive into Deep Learning — Zhang, Lipton, Li, SmolabookCited by: Backpropagation
43. Dive into Deep Learning — Zhang, Lipton, Li, SmolabookCited by: Batch normalization
44. Dive into Deep Learning — Zhang, Lipton, Li, SmolabookCited by: Bayes' theorem
45. Dive into Deep Learning — Zhang, Lipton, Li, SmolabookCited by: Decoding
46. Dive into Deep Learning — Zhang, Lipton, Li, SmolabookCited by: Eigenvectors and eigenvalues
47. Dive into Deep Learning — Zhang, Lipton, Li, SmolabookCited by: Expectation and variance
48. Dive into Deep Learning — Zhang, Lipton, Li, SmolabookCited by: Language model, Perplexity
49. Dive into Deep Learning — Zhang, Lipton, Li, SmolabookCited by: Maximum likelihood
50. Dive into Deep Learning — Zhang, Lipton, Li, SmolabookCited by: Recurrent neural network
51. Dive into Deep Learning — 12.3 Gradient Descent — Zhang, Lipton, Li, SmolabookCited by: Learning rate
52. Dive into Deep Learning — 2.6 Probability and Statistics — Zhang, Lipton, Li, SmolabookCited by: Bayes' theorem
53. Dive into Deep Learning — 3.1 Linear Regression — Zhang, Lipton, Li, SmolabookCited by: Huber loss, Linear regression, MAE, MSE, RMSE
54. Dive into Deep Learning — 3.6 Generalization — Zhang, Lipton, Li, SmolabookCited by: MSE, R², Train-test split
55. Dive into Deep Learning — 4.1 Softmax Regression — Zhang, Lipton, Li, SmolabookCited by: Softmax
56. Dive into Deep Learning — Anchor Boxes — Zhang, Lipton, Li, SmolabookCited by: Intersection over union, Mean average precision, Object detection
57. Dive into Deep Learning — Attention Pooling by Similarity — Zhang, Lipton, Li, SmolabookCited by: Distance-weighted KNN
58. Dive into Deep Learning — Backpropagation Through Time — Zhang, Lipton, Li, SmolabookCited by: Recurrent neural network
59. Dive into Deep Learning — Computer Vision — Zhang, Lipton, Li, SmolabookCited by: Image classification, Vision tasks
60. Dive into Deep Learning — Dropout — Zhang, Lipton, Li, SmolabookCited by: Dropout
61. Dive into Deep Learning — Fully Convolutional Networks — Zhang, Lipton, Li, SmolabookCited by: Semantic segmentation
62. Dive into Deep Learning — Generalization in Deep Learning — Zhang, Lipton, Li, SmolabookCited by: Early stopping
63. Dive into Deep Learning — Information Theory — Zhang, Lipton, Li, SmolabookCited by: Cross-entropy, Entropy, Classification metrics
64. Dive into Deep Learning — Learning Rate Scheduling — Zhang, Lipton, Li, SmolabookCited by: Early stopping
65. Dive into Deep Learning — Multiple Input and Multiple Output Channels — Zhang, Lipton, Li, SmolabookCited by: Depthwise separable convolution
66. Dive into Deep Learning — Natural Language Processing: Applications — Zhang, Lipton, Li, SmolabookCited by: NLP tasks
67. Dive into Deep Learning — Semantic Segmentation — Zhang, Lipton, Li, SmolabookCited by: Semantic segmentation
68. Dive into Deep Learning — Sequence-to-Sequence Learning — Zhang, Lipton, Li, SmolabookCited by: BLEU, Sequence-to-sequence, Text metrics
69. Dive into Deep Learning — Transformers for Vision — Zhang, Lipton, Li, SmolabookCited by: Vision transformer
70. Dive into Deep Learning — Transposed Convolution — Zhang, Lipton, Li, SmolabookCited by: Transposed convolution
71. Do Better ImageNet Models Transfer Better? — Kornblith, Shlens, Le (2019)paperCited by: Transfer learning
72. Domain Adaptive Transfer Learning with Specialist Models — Ngiam, Peng, Vasudevan, Kornblith, Le, Pang (2018)paperCited by: Transfer learning
73. e-Handbook of Statistical Methods — 1.3.3.1 Autocorrelation Plot — NIST/SEMATECHstandardCited by: Autocorrelation
74. e-Handbook of Statistical Methods — 1.3.3.26 Scatter Plot — NIST/SEMATECHstandardCited by: Correlation
75. e-Handbook of Statistical Methods — 1.3.5.17 Detection of Outliers — NIST/SEMATECHstandardCited by: Huber loss, Outliers
76. e-Handbook of Statistical Methods — 1.3.6.6.3 Cauchy Distribution — NIST/SEMATECHstandardCited by: Expectation and variance
77. e-Handbook of Statistical Methods — 4.1.4.1 Linear Least Squares Regression — NIST/SEMATECHstandardCited by: Linear regression, Outliers
78. e-Handbook of Statistical Methods — 4.2.1 Typical underlying assumptions in process modeling — NIST/SEMATECHstandardCited by: Linear regression
79. e-Handbook of Statistical Methods — 4.4.4 How can I tell if a model fits my data? — NIST/SEMATECHstandardCited by: R²
80. e-Handbook of Statistical Methods — 6.4.4.6 Box-Jenkins Model Identification — NIST/SEMATECHstandardCited by: Autocorrelation, Stationarity
81. Effectively Unbiased FID and Inception Score and where to find them — Chong, Forsyth (2020)paperCited by: Fréchet Inception Distance
82. Efficient BackProp — LeCun, Bottou, Orr, Müller (1998)paperCited by: Sigmoid, Tanh
83. Efficient Estimation of Word Representations in Vector Space — Mikolov, Chen, Corrado, Dean (2013)paperCited by: Word embeddings
84. ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators — Clark, Luong, Le, Manning (2020)paperCited by: Transformer family
85. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer — Raffel, Shazeer, Roberts, Lee, Narang, Matena, Zhou, Li, Liu (2020)paperCited by: Transformer family
86. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks — Ren, He, Girshick, Sun (2016)paperCited by: Mean average precision, Object detection
87. Finetuned Language Models Are Zero-Shot Learners — Wei, Bosma, Zhao, Guu, Yu, Lester, Du, Dai, Le (2022)paperCited by: Instruction tuning
88. Focal Loss for Dense Object Detection — Lin, Goyal, Girshick, He, Dollár (2018)paperCited by: Mean average precision, Object detection
89. Forecasting: Principles and Practice — Hyndman, AthanasopoulosbookCited by: MAPE
90. Forecasting: Principles and Practice — Hyndman, AthanasopoulosbookCited by: MSLE
91. Forecasting: theory and practice — Petropoulos, Apiletti, Assimakopoulos et al. (2022)paperCited by: MAPE, Time series decomposition, Forecasting methods
92. GAIN: Missing Data Imputation using Generative Adversarial Nets — Yoon, Jordon, van der Schaar (2018)paperCited by: Missing data
93. Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression — Rezatofighi, Tsoi, Gwak, Sadeghian, Reid, Savarese (2019)paperCited by: Intersection over union
94. Generative Adversarial Nets — Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, Bengio (2014)paperCited by: Diffusion model, Generative adversarial network
95. Group Normalization — Wu, He (2018)paperCited by: Batch normalization, Mini-batch gradient descent
96. Hidden Technical Debt in Machine Learning Systems — Sculley, Holt, Golovin, Davydov, Phillips, Ebner, Chaudhary, Young, Crespo, Dennison (2015)paperCited by: Feature engineering, Machine learning workflow
97. High-Resolution Image Synthesis with Latent Diffusion Models — Rombach, Blattmann, Lorenz, Esser, Ommer (2022)paperCited by: Diffusion model, Generative adversarial network
98. How Does Batch Normalization Help Optimization? — Santurkar, Tsipras, Ilyas, Madry (2018)paperCited by: Batch normalization
99. How to Use t-SNE Effectively — Wattenberg, Viégas, Johnson (2016)paperCited by: Manifold learning
100. How transferable are features in deep neural networks? — Yosinski, Clune, Bengio, Lipson (2014)paperCited by: Transfer learning
101. Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization — Li, Jamieson, DeSalvo, Rostamizadeh, Talwalkar (2018)paperCited by: Hyperparameter tuning
102. ImageNet Large Scale Visual Recognition Challenge — Russakovsky, Deng, Su, Krause, Satheesh, Ma, Huang, Karpathy, Khosla, Bernstein, Berg, Fei-Fei (2015)paperCited by: Image classification
103. Introduction to Information Retrieval — Manning, Raghavan, Schütze (2008)bookCited by: Accuracy, Bag of words, Confusion matrix, Eigenvectors and eigenvalues, F1-score, Precision, Recall, ROC AUC, Specificity, TF-IDF
104. Introduction to Information Retrieval — Manning, Raghavan, Schütze (2008)bookCited by: Sentence embeddings
105. Introduction to Information Retrieval — Manning, Raghavan, SchützebookCited by: Classification metrics
106. Introduction to Information Retrieval — Support vector machines — Manning, Raghavan, Schütze (2008)bookCited by: SVM
107. Language Models are Few-Shot Learners — Brown, Mann, Ryder, Subbiah, Kaplan, Dhariwal, et al. (2020)paperCited by: Instruction tuning, Prompting, NLP tasks, Transformer family
108. Layer Normalization — Ba, Kiros, Hinton (2016)paperCited by: Batch normalization, Transformer
109. Leakage and the Reproducibility Crisis in ML-based Science — Kapoor, Narayanan (2023)paperCited by: Data leakage, Validation strategies
110. Learning Transferable Visual Models From Natural Language Supervision — Radford, Kim, Hallacy, Ramesh, Goh, Agarwal, Sastry, Askell, Mishkin, Clark, Krueger, Sutskever (2021)paperCited by: CLIP, Self-supervised learning, Vision tasks
111. Learning Transferable Visual Models From Natural Language Supervision — Radford, Kim, Hallacy, Ramesh, Goh, Agarwal, Sastry, Askell, Mishkin, Clark, Krueger, Sutskever (2021)paperCited by: Vision transformer
112. LightGBM: A Highly Efficient Gradient Boosting Decision Tree — Ke, Meng, Finley, Wang, Chen, Ma, Ye, Liu (2017)paperCited by: Gradient boosting, Tree ensembles
113. Machine Learning Crash Course — Classification: accuracy, recall, precision, and related metrics — GooglecourseCited by: Accuracy, Confusion matrix, F1-score, Precision, Recall, ROC AUC, Specificity, Classification metrics
114. Machine Learning Crash Course — ROC and AUC — GooglecourseCited by: ROC AUC, Specificity
115. Machine Learning Crash Course — Thresholds and the confusion matrix — GooglecourseCited by: Accuracy, Confusion matrix, Cross-entropy, Recall
116. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings — Bolukbasi, Chang, Zou, Saligrama, Kalai (2016)paperCited by: Word embeddings
117. Mask R-CNN — He, Gkioxari, Dollár, Girshick (2017)paperCited by: Instance segmentation
118. Microsoft COCO: Common Objects in Context — Lin, Maire, Belongie, Bourdev, Girshick, Hays, Perona, Ramanan, Dollár, Zitnick (2014)paperCited by: Instance segmentation, Vision tasks
119. minimize(method='L-BFGS-B')docsCited by: BFGS
120. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications — Howard, Zhu, Chen, Kalenichenko, Wang, Weyand, Andreetto, Adam (2017)paperCited by: Depthwise separable convolution
121. Neural Architectures for Named Entity Recognition — Lample, Ballesteros, Subramanian, Kawakami, Dyer (2016)paperCited by: Named entity recognition
122. Neural Machine Translation by Jointly Learning to Align and Translate — Bahdanau, Cho, Bengio (2015)paperCited by: Attention, Sequence-to-sequence
123. Neural Machine Translation of Rare Words with Subword Units — Sennrich, Haddow, Birch (2016)paperCited by: Tokenization
124. NIST/SEMATECH e-Handbook — How can we make multiple comparisons?docsCited by: Multiple comparisons
125. NIST/SEMATECH e-Handbook of Statistical MethodsstandardCited by: Autocorrelation
126. NIST/SEMATECH e-Handbook of Statistical MethodsstandardCited by: Bootstrap
127. NIST/SEMATECH e-Handbook of Statistical MethodsstandardCited by: Central limit theorem
128. NIST/SEMATECH e-Handbook of Statistical MethodsstandardCited by: Confidence interval
129. NIST/SEMATECH e-Handbook of Statistical MethodsstandardCited by: Hypothesis testing
130. NIST/SEMATECH e-Handbook of Statistical MethodsstandardCited by: Stationarity
131. NIST/SEMATECH e-Handbook of Statistical MethodsstandardCited by: Time series decomposition, Forecasting methods
132. NIST/SEMATECH e-Handbook of Statistical Methods — Product and Process ComparisonsstandardCited by: A/B testing
133. On Calibration of Modern Neural Networks — Guo, Pleiss, Sun, Weinberger (2017)paperCited by: Cross-entropy, Model calibration, Classification metrics
134. On Calibration of Modern Neural Networks — Guo, Pleiss, Sun, Weinberger (2017)paperCited by: Sigmoid
135. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes — Ng, Jordan (2001)paperCited by: Gaussian discriminant analysis
136. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation — Qi, Su, Mo, Guibas (2017)paperCited by: PointNet
137. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space — Qi, Yi, Su, Guibas (2017)paperCited by: PointNet
138. Project Jupyter documentationdocsCited by: Jupyter Notebook
139. PyTorch documentation — torch.nn.Conv2ddocsCited by: Convolution
140. Random Search for Hyper-Parameter Optimization — Bergstra, Bengio (2012)paperCited by: Hyperparameter tuning
141. Reconciling modern machine learning practice and the bias-variance trade-off — Belkin, Hsu, Ma, Mandal (2019)paperCited by: Bias-variance tradeoff
142. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks — Lewis, Perez, Piktus, Petroni, Karpukhin, Goyal, Küttler, Lewis, Yih, Rocktäschel, Riedel, Kiela (2021)paperCited by: Hallucination, Retrieval-augmented generation
143. RoBERTa: A Robustly Optimized BERT Pretraining Approach — Liu, Ott, Goyal, Du, Joshi, Chen, Levy, Lewis, Zettlemoyer, Stoyanov (2019)paperCited by: Transformer family
144. Rules of Machine Learning: Best Practices for ML Engineering (Google) — ZinkevichdocsCited by: Feature engineering, Machine learning workflow
145. scikit-learn — Common pitfalls in the interpretation of coefficients of linear modelsdocsCited by: Confounding
146. scikit-learn — DecisionTreeClassifierdocsCited by: Decision tree
147. scikit-learn — KMeansdocsCited by: K-means
148. scikit-learn — KNeighborsClassifierdocsCited by: Distance-weighted KNN, KNN
149. scikit-learn — KNNImputerdocsCited by: KNN
150. scikit-learn — sklearn.linear_model.BayesianRidgedocsCited by: Bayesian linear regression
151. scikit-learn — sklearn.linear_model.ElasticNetdocsCited by: Elastic Net, Linear model family
152. scikit-learn — sklearn.linear_model.HuberRegressordocsCited by: Huber loss, MAE
153. scikit-learn — sklearn.linear_model.LassodocsCited by: Lasso regression
154. scikit-learn — sklearn.linear_model.RidgedocsCited by: Regularization, Ridge regression
155. scikit-learn — sklearn.metrics.accuracy_scoredocsCited by: Accuracy
156. scikit-learn — sklearn.metrics.confusion_matrixdocsCited by: Confusion matrix, Specificity, Classification metrics
157. scikit-learn — sklearn.metrics.f1_scoredocsCited by: F1-score
158. scikit-learn — sklearn.metrics.mean_absolute_errordocsCited by: MAE
159. scikit-learn — sklearn.metrics.mean_absolute_percentage_errordocsCited by: MAPE
160. scikit-learn — sklearn.metrics.mean_squared_errordocsCited by: MSE, Regression metrics
161. scikit-learn — sklearn.metrics.mean_squared_log_errordocsCited by: MSLE
162. scikit-learn — sklearn.metrics.precision_scoredocsCited by: Precision
163. scikit-learn — sklearn.metrics.r2_scoredocsCited by: R²
164. scikit-learn — sklearn.metrics.recall_scoredocsCited by: Recall
165. scikit-learn — sklearn.metrics.roc_auc_scoredocsCited by: ROC AUC
166. scikit-learn — sklearn.metrics.root_mean_squared_errordocsCited by: RMSE
167. scikit-learn — sklearn.preprocessing.PolynomialFeaturesdocsCited by: Polynomial regression
168. scikit-learn — SplineTransformerdocsCited by: Polynomial regression
169. scikit-learn — SVCdocsCited by: SVM
170. scikit-learn User Guide — ClusteringdocsCited by: K-means, PCA, Clustering methods
171. scikit-learn User Guide — Common pitfalls and recommended practicesdocsCited by: Data leakage
172. scikit-learn User Guide — Cross-validationdocsCited by: Autocorrelation, Cross-validation, Machine learning workflow, Overfitting, Train-test split, Validation strategies
173. scikit-learn User Guide — Decision TreesdocsCited by: Decision tree, Entropy, Gradient
174. scikit-learn User Guide — Ensembles: Gradient boosting, random forests, bagging, voting, stackingdocsCited by: Bagging, Boosting, Gradient boosting, Random forest, Tree ensembles
175. scikit-learn User Guide — Feature extraction from textdocsCited by: Bag of words, TF-IDF
176. scikit-learn User Guide — Imputation of missing valuesdocsCited by: Missing data, Missing-data methods
177. scikit-learn User Guide — Linear and Quadratic Discriminant AnalysisdocsCited by: Gaussian discriminant analysis
178. scikit-learn user guide — Linear ModelsdocsCited by: Elastic Net, Generalized linear models, Lasso regression, Linear regression, Perceptron
179. scikit-learn User Guide — Metrics and scoringdocsCited by: Class imbalance, Cross-entropy, MAPE, MSE, MSLE, R², Recall, RMSE
180. scikit-learn user guide — Nearest NeighborsdocsCited by: KNN
181. scikit-learn User Guide — Novelty and Outlier DetectiondocsCited by: Outliers
182. scikit-learn User Guide — Permutation feature importancedocsCited by: Feature importance
183. scikit-learn User Guide — Preprocessing datadocsCited by: Feature engineering
184. scikit-learn User Guide — Probability calibrationdocsCited by: Model calibration, Perceptron
185. scikit-learn User Guide — Stochastic Gradient DescentdocsCited by: Perceptron
186. scikit-learn User Guide — Support Vector MachinesdocsCited by: Perceptron
187. scikit-learn User Guide — Tuning the hyper-parameters of an estimatordocsCited by: Bias-variance tradeoff, Hyperparameter tuning
188. SciPy — scipy.stats.false_discovery_controldocsCited by: Multiple comparisons, Multiple-testing corrections
189. SciPy reference — scipy.stats.bootstrapdocsCited by: Bootstrap
190. scipy.optimize.minimizedocsCited by: BFGS, Conjugate gradient
191. scipy.sparse.linalg.cgdocsCited by: Conjugate gradient
192. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks — Reimers, Gurevych (2019)paperCited by: CLIP, Retrieval-augmented generation, Sentence embeddings, NLP tasks
193. SentencePiece: A simple and language independent subword tokenizer and detokenizer — Kudo, Richardson (2018)paperCited by: Named entity recognition, Tokenization
194. sklearn.ensemble.GradientBoostingClassifierdocsCited by: Early stopping
195. sklearn.linear_model.LogisticRegressiondocsCited by: BFGS, Logistic regression
196. statsmodels — Generalized Linear ModelsdocsCited by: Generalized linear models
197. statsmodels — multipletestsdocsCited by: Multiple comparisons, Multiple-testing corrections
198. Survey of Hallucination in Natural Language Generation — Ji, Lee, Frieske, Yu, Su, Xu, Ishii, Bang, Chen, Dai, Chan, Madotto, Fung (2023)paperCited by: Hallucination
199. Tangled up in BLEU — Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics — Mathur, Baldwin, Cohn (2020)paperCited by: BLEU, Text metrics
200. Ten Simple Rules for Reproducible Research in Jupyter Notebooks — Rule, Birmingham, Zuniga, Altintas, Huang, Knight, Moshiri, Nguyen, Rosenthal, Pérez, Rose (2018)paperCited by: Jupyter Notebook
201. tf.keras.optimizers.AdamdocsCited by: Adam
202. The Curious Case of Neural Text Degeneration — Holtzman, Buys, Du, Forbes, Choi (2020)paperCited by: Decoding, Hallucination, Language model, Perplexity
203. The Marginal Value of Adaptive Gradient Methods in Machine Learning — Wilson, Roelofs, Stern, Srebro, Recht (2017)paperCited by: Adam
204. Theoretical Analysis of the k-Means Algorithm — A Survey — Blömer, Lammersen, Schmidt, Sohler (2016)paperCited by: K-means, Clustering methods
205. Think Bayes — DowneybookCited by: Bayes' theorem
206. Think Stats — DowneybookCited by: Autocorrelation, Bootstrap, Central limit theorem, Confidence interval, Expectation and variance, Hypothesis testing, Maximum likelihood, Missing data, Outliers, Stationarity, Time series decomposition
207. Think Stats — DowneybookCited by: Missing-data methods
208. Think Stats — 10. Linear least squares — Allen B. DowneybookCited by: Correlation, R², RMSE
209. Think Stats — Ch. 11, Regression — DowneybookCited by: Generalized linear models
210. Think Stats — Ch. 2, Distributions — DowneybookCited by: Huber loss
211. Think Stats — Ch. 7, Relationships between variables — Allen B. DowneybookCited by: A/B testing, Confounding, Correlation
212. Think Stats — Ch. 9, Hypothesis Testing — DowneybookCited by: A/B testing, Multiple comparisons, Multiple-testing corrections
213. torch.utils.data.DataLoaderdocsCited by: Mini-batch gradient descent
214. Torchvision documentation — Transforming images, videos, boxes and moredocsCited by: Data augmentation
215. Training Language Models to Follow Instructions with Human Feedback — Ouyang, Wu, Jiang, Almeida, Wainwright, Mishkin, et al. (2022)paperCited by: Hallucination, Instruction tuning, Retrieval-augmented generation
216. Transfusion: Understanding Transfer Learning for Medical Imaging — Raghu, Zhang, Kleinberg, Bengio (2019)paperCited by: Transfer learning
217. U-Net: Convolutional Networks for Biomedical Image Segmentation — Ronneberger, Fischer, Brox (2015)paperCited by: Diffusion model, Semantic segmentation, Transposed convolution
218. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction — McInnes, Healy, Melville (2020)paperCited by: Manifold learning
219. Understanding Random Forests: From Theory to Practice — Louppe (2014)paperCited by: Feature importance, Random forest
220. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks — Luo, Li, Urtasun, Zemel (2017)paperCited by: Pooling, Receptive field, Residual connection
221. XGBoost: A Scalable Tree Boosting System — Chen, Guestrin (2016)paperCited by: Boosting, Tree ensembles
222. XGBoost: A Scalable Tree Boosting System — Chen, Guestrin (2016)paperCited by: Gradient boosting
223. YOLO9000: Better, Faster, Stronger — Redmon, Farhadi (2017)paperCited by: Object detection
224. You Only Look Once: Unified, Real-Time Object Detection — Redmon, Divvala, Girshick, Farhadi (2016)paperCited by: Object detection