├── LICENSE └── README.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Roozbeh Sanaei 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Statistics and Machine Learning Blogs 2 | The objective of this compilation is to bring together a variety of resources that provide straightforward and accessible explanations of fundamental principles in statistics and machine learning. 3 | 4 | [Defintions](https://docs.google.com/document/d/15s3CQWFRn-HrBmbDfMZz6LtSqr9TT-lsZteaFNZPky8/edit?usp=sharing) 5 | 6 | ## Probability 7 | [Set theory](https://stats.libretexts.org/Bookshelves/Probability_Theory/Probability_Mathematical_Statistics_and_Stochastic_Processes_(Siegrist)/01%3A_Foundations/1.01%3A_Sets)\ 8 | [Venn Diagrams](https://www.researchgate.net/figure/A-Venn-diagram-of-unions-and-intersections-for-two-sets-A-and-B-and-their-complements_fig1_332453167)\ 9 | [Probability Axioms](https://math.unm.edu/~james/Probability2.pdf)\ 10 | [pdf,cdf,ppt](https://www.itl.nist.gov/div898/handbook/eda/section3/eda362.htm)\ 11 | [Quantiles](https://prepnuggets.com/glossary/quantile/)\ 12 | [Experiment, Sample space, Event, Probability function, Random variable](http://www.cs.toronto.edu/~anikolov/CSC473W20/Probability.pdf)\ 13 | [Properties of cdf and pdf](https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2014/bc2aecd33d981fe4625fa8a434a9dca7_MIT18_05S14_Reading7a.pdf)\ 14 | [Transformations of Random Variables](http://www2.econ.iastate.edu/classes/econ671/hallam/documents/Transformations.pdf)\ 15 | [Joint Probability Distribution](https://en.wikipedia.org/wiki/Joint_probability_distribution)\ 16 | [Expected Values](https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2014/79a290ef627e8f975fcbf93ff869d8ec_MIT18_05S14_Reading4b.pdf), 17 | [Properties of Expected Value](https://en.wikipedia.org/wiki/Expected_value#Properties)\ 18 | [Variance Values](https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2014/b09f8047e25079e59d379a4d9782b621_MIT18_05S14_Reading5a.pdf), 19 | [Properties of Variance](https://en.wikipedia.org/wiki/Variance#Properties)\ 20 | [Bayes Rule](https://towardsdatascience.com/bayes-theorem-the-holy-grail-of-data-science-55d93315defb) 21 | 22 | ### Probability Distributions 23 | [Univariate distributions](http://www.math.wm.edu/~leemis/chart/UDR/UDR.html)\ 24 | [Bernoulli distribution](https://en.wikipedia.org/wiki/Bernoulli_distribution)\ 25 | [Binomial distribution](https://en.wikipedia.org/wiki/Binomial_distribution)\ 26 | [Continous Uniform Distribution](https://en.wikipedia.org/wiki/Continuous_uniform_distribution)\ 27 | [Poission distribution](https://www.le.ac.uk/users/dsgp1/COURSES/LEISTATS/poisson.pdf)\ 28 | [Exponential distribution](https://stats.stackexchange.com/questions/2092/relationship-between-poisson-and-exponential-distribution) 29 | 30 | 31 | ## Statistics 32 | 33 | ### General Concepts 34 | [Probablity vs Statistics](https://stats.stackexchange.com/questions/665/whats-the-difference-between-probability-and-statistics)\ 35 | [Likelihoodist, Bayesian, and Frequentist Methods](http://gandenberger.org/2014/07/21/intro-to-statistical-methods/)\ 36 | [Mathematical Basis of Bayesian vs Frequentist Debate](https://stats.stackexchange.com/questions/230415/is-there-any-mathematical-basis-for-the-bayesian-vs-frequentist-debate) 37 | 38 | 39 | ### Correlation 40 | [Covariance and Correlation](https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/readings/MIT18_05S14_Reading7b.pdf)\ 41 | [Pearson Correlation](https://stats.libretexts.org/Bookshelves/Introductory_Statistics/Book%3A_Statistics_Using_Technology_(Kozak)/10%3A_Regression_and_Correlation/10.02%3A_Correlation)\ 42 | [Partial Correlation](https://towardsdatascience.com/partial-correlation-508353cd8b5)\ 43 | [Kendall Rank Correlation](https://www.statisticshowto.com/kendalls-tau/)\ 44 | [Wald Wolfowitz Run Test](https://accendoreliability.com/the-wald-wolfowitz-run-test-for-two-small-samples/) 45 | 46 | 47 | ### Estimators 48 | [Estimators](https://stats.libretexts.org/Bookshelves/Probability_Theory/Probability_Mathematical_Statistics_and_Stochastic_Processes_(Siegrist)/07%3A_Point_Estimation/7.01%3A_Estimators)\ 49 | [Difference between estimator and statistics](https://stats.stackexchange.com/questions/47728/what-is-the-difference-between-an-estimator-and-a-statistic)\ 50 | [Maximum Likelihood Estimation](https://online.stat.psu.edu/stat504/lesson/1/1.5)\ 51 | [MLE vs MAP Estimation](https://medium.com/towards-data-science/a-gentle-introduction-to-maximum-likelihood-estimation-and-maximum-a-posteriori-estimation-d7c318f9d22d)\ 52 | [MLE vs MAP Bayesian Inference](https://medium.com/towards-data-science/mle-map-and-bayesian-inference-3407b2d6d4d9) 53 | 54 | ### Hypothesis Testing 55 | [Law of Large Numbers](https://www.probabilitycourse.com/chapter7/7_1_1_law_of_large_numbers.php)\ 56 | [Difference between Strong and Weak laws of large numbers](https://math.stackexchange.com/questions/2024255/what-is-the-difference-between-the-weak-and-strong-law-of-large-numbers)\ 57 | [Central Limit Theorem](https://statisticsbyjim.com/basics/central-limit-theorem/)\ 58 | [Effect Size](https://statisticsbyjim.com/basics/effect-sizes-statistics/) 59 | [p-value](https://www.scribbr.com/statistics/p-value/)\ 60 | [Right, Left, and Two tailed test](https://courses.lumenlearning.com/wm-concepts-statistics/chapter/hypothesis-test-for-difference-in-two-population-proportions-4-of-6/)\ 61 | [Type I and Type II Errors](https://www.scribbr.com/statistics/type-i-and-type-ii-errors/)\ 62 | [SVD and PCA](https://medium.com/@jonathan-hui/machine-learning-singular-value-decomposition-svd-principal-component-analysis-pca-1d45e885e491) 63 | 64 | #### Z-test 65 | [Z-Score](https://statisticsbyjim.com/basics/z-score/)\ 66 | [One Proportion Z-test](https://www.statology.org/one-proportion-z-test/)\ 67 | [Two Sample Z-test](http://www.stat.ucla.edu/~cochran/stat10/winter/lectures/lect21.html) 68 | 69 | #### t-rest 70 | [t-distribution](https://en.wikipedia.org/wiki/Student%27s_t-distribution#How_Student's_distribution_arises_from_sampling)\ 71 | [Paired t-test](https://www.technologynetworks.com/informatics/articles/paired-vs-unpaired-t-test-differences-assumptions-and-hypotheses-330826)\ 72 | [UnPaird t-test, Pooled t-test, Welch's t-test](https://en.wikipedia.org/wiki/Student%27s_t-test#Independent_two-sample_t-test) 73 | 74 | #### Chi-square test 75 | [Chi-square distribution](https://en.wikipedia.org/wiki/Chi-squared_distribution)\ 76 | [Pearson's Theorem](https://ocw.mit.edu/courses/18-443-statistics-for-applications-fall-2003/708680f9de8209158ca6462577a46a56_lec23.pdf)\ 77 | [Chi-square test](https://en.wikipedia.org/wiki/Chi-squared_test) 78 | 79 | #### Non-Paramteric Tests 80 | [Nonparametric Tests vs. Parametric Tests](https://statisticsbyjim.com/hypothesis-testing/nonparametric-parametric-tests)\ 81 | [Mann Whitney U Test (Wilcoxon Rank Sum Test)](https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_nonparametric/bs704_nonparametric4.html)\ 82 | [Wilcoxon Signed Rank Test](https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_nonparametric/BS704_Nonparametric5.html#headingtaglink_3)\ 83 | [Sign Test](https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_nonparametric/BS704_Nonparametric5.html#headingtaglink_3)\ 84 | [The Kruskal-Wallis Test](https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_nonparametric/BS704_Nonparametric7.html)\ 85 | [Permutation Test](https://www.jwilber.me/permutationtest/) 86 | 87 | 88 | ### Linear Regression 89 | [Ordinary Least Squares through minimising the sum of square errors](https://www.timlrx.com/blog/notes-on-regression-ols)\ 90 | [Projection and Orthogonality](https://www.timlrx.com/blog/notes-on-regression-geometry)\ 91 | [Method of Moments](https://en.wikipedia.org/wiki/Method_of_moments_(statistics))\ 92 | [Linear Regression as Maximum Likelihoods](https://www.timlrx.com/blog/notes-on-regression-maximum-likelihood)\ 93 | [Regression vs Correlation coefficients](https://www.graphpad.com/support/faq/what-is-the-difference-between-correlation-and-linear-regression/)\ 94 | [Bayesian Linear Regression](https://www.inovex.de/de/blog/bayesian-linear-regression-in-machine-learning/)\ 95 | [Applying SVD to Linear Regression](https://www.timlrx.com/blog/notes-on-regression-singular-vector-decomposition)\ 96 | [Linear Regression Metrics](https://towardsdatascience.com/how-to-choose-the-best-linear-regression-model-a-comprehensive-guide-for-beginners-754480768467) 97 | 98 | #### Multi Linear Regression 99 | [Variance Inflation Factors](https://statisticsbyjim.com/regression/variance-inflation-factors/)\ 100 | [Multi Linear Regression and multicollinearity](https://towardsdatascience.com/multiple-linear-regression-8cf3bee21d8b)[and also](https://statisticsbyjim.com/regression/multicollinearity-in-regression-analysis/) 101 | 102 | #### Bias-Variance 103 | [Bias-Variance Decomposition of the Squared Loss](http://rasbt.github.io/mlxtend/user_guide/evaluate/bias_variance_decomp/)\ 104 | [Bias-Variance Trade-off and Double Descent](https://mlu-explain.github.io/bias-variance/)\ 105 | [Regualization: the path to Bias-Variance Trade-off](https://medium.com/towards-data-science/regularization-the-path-to-bias-variance-trade-off-b7a7088b4577) 106 | 107 | ### Multiple Hypothesis Testing 108 | #### F-Test 109 | [F-distribution](https://en.wikipedia.org/wiki/F-distribution)\ 110 | [General Linear F-test](https://online.stat.psu.edu/stat462/node/135/)\ 111 | [Calculating F-Statistic](https://www.mattblackwell.org/files/teaching/ftests.pdf)\ 112 | [Coding Systems For Categorical Variables](https://stats.oarc.ucla.edu/spss/faq/coding-systems-for-categorical-variables-in-regression-analysis/) 113 | #### ANOVA 114 | [What is ANOVA](https://www.spss-tutorials.com/anova-what-is-it/)\ 115 | [One Way Anova](https://www.itl.nist.gov/div898/handbook/prc/section4/prc431.htm)\ 116 | [ANOVA mathematical model](https://www.itl.nist.gov/diBv898/handbook/prc/section4/prc432.htm)\ 117 | [ANOVA Assumptions](https://sites.ualberta.ca/~lkgray/uploads/7/3/6/2/7362679/slides_-_anova_assumptions.pdf)\ 118 | [Linear Combinations and Contrasts](http://users.stat.umn.edu/~helwig/notes/aov1-Notes.pdf)\ 119 | [Fixed Effect, Random Effect and Mixed Effect models](https://stats.stackexchange.com/questions/4700/what-is-the-difference-between-fixed-effect-random-effect-and-mixed-effect-mode)\ 120 | [Factorial and Unbalanced ANOVA](http://users.stat.umn.edu/~helwig/notes/aov2-Notes.pdf)\ 121 | [ANCOVA](http://users.stat.umn.edu/~helwig/notes/acov-Notes.pdf) 122 | 123 | #### Multiple Comparision Problem 124 | [Multiple Comparison Problem](https://towardsdatascience.com/an-overview-of-methods-to-address-the-multiple-comparison-problem-310427b3ba92)\ 125 | [Bonferroni’s Correction](http://users.stat.umn.edu/~helwig/notes/aov1-Notes.pdf)\ 126 | [Holm’s Step-Down and Hochberg’s Step-Up Procedure](https://en.wikipedia.org/wiki/Family-wise_error_rate)\ 127 | [Studentized range distribution](https://en.wikipedia.org/wiki/Studentized_range_distribution)\ 128 | [Turkey's Range Test](http://users.stat.umn.edu/~helwig/notes/OneWayANOVA.pdf) 129 | 130 | #### Multivariate Hypothesis Testing 131 | [MANOVA](https://online.stat.psu.edu/stat505/lesson/8)\ 132 | [PCA](https://online.stat.psu.edu/stat505/lesson/11)\ 133 | [Factor Analysis](https://online.stat.psu.edu/stat505/lesson/12)\ 134 | [Canonical Analysis](https://online.stat.psu.edu/stat505/lesson/13) 135 | 136 | #### Structure Equation Modeling 137 | [Basics](http://davidakenny.net/cm/causalm.htm)\ 138 | [Tutorial](https://psu-psychology.github.io/psy-597-SEM/) 139 | 140 | 141 | #### Statistical Paradoxes 142 | [Monty Hall](https://betterexplained.com/articles/understanding-the-monty-hall-problem/)\ 143 | [Russels Paradox](https://www.quora.com/What-in-laymans-terms-is-Russells-Paradox) 144 | 145 | 146 | 147 | ### Bayesian Statistics 148 | [Bayesian Learning](https://www.analyticsvidhya.com/blog/2016/06/bayesian-statistics-beginners-simple-english/)\ 149 | [A/B testing, Bayesian](https://app.datacamp.com/workspace/w/cff27c6e-b68b-42df-ad82-9b0d029b7f0d)\ 150 | [Hierarchical Modeling](http://mfviz.com/hierarchical-models/) 151 | 152 | #### Bayesian Samplers 153 | [Rejection Sampling](https://towardsdatascience.com/what-is-rejection-sampling-1f6aff92330d)\ 154 | [Importance Sampling](https://towardsdatascience.com/importance-sampling-introduction-e76b2c32e744)\ 155 | [Inverse Transform Sampling](https://towardsdatascience.com/an-insight-on-generating-samples-from-a-custom-probability-density-function-d0a06c290c54)\ 156 | [The Metropolis-hasting algorithm](https://medium.com/towards-data-science/mcmc-intuition-for-everyone-5ae79fff22b1)[ and also](https://www2.math.upenn.edu/~bmor/Metropolis.pdf)\ 157 | [Gibbs Sampling](https://towardsdatascience.com/gibbs-sampling-explained-b271f332ed8d)\ 158 | [Gibbs Sampling as a Special Case of Metropolis–Hastings](https://gregorygundersen.com/blog/2020/02/23/gibbs-sampling/) 159 | 160 | ### Causal Inference 161 | [Structural Causal Models](https://medium.data4sci.com/causal-inference-part-iv-structural-causal-models-df10a83be580)\ 162 | [Chains, and Forks](https://medium.data4sci.com/causal-inference-part-v-chains-and-forks-7b0b088c346e)\ 163 | [Colliders](https://medium.data4sci.com/causal-inference-part-vi-colliders-af07301c9a15)\ 164 | [d-separation](https://medium.data4sci.com/causal-inference-part-vii-d-separation-aa74e361d34e)\ 165 | [Model Testing and Causal Search](https://medium.data4sci.com/causal-inference-part-vii-d-separation-aa74e361d34e)\ 166 | [Interventions](https://medium.data4sci.com/causal-inference-part-ix-interventions-c3f94190191d)\ 167 | [The Adjustment Formula](https://medium.data4sci.com/causal-inference-part-x-the-adjustment-formula-f9668469d76)\ 168 | [Backdoor Criterion](https://medium.data4sci.com/causal-inference-part-xi-backdoor-criterion-e29627a1da0e)\ 169 | [Front-door Criterion](https://medium.data4sci.com/causal-inference-part-xii-front-door-criterion-38bec5172f3e) 170 | 171 | [Gaussian Process](https://thegradient.pub/gaussian-process-not-quite-for-dummies/)\ 172 | [Bootstrapping](https://online.stat.psu.edu/stat500/lesson/11/11.2) 173 | 174 | 175 | 176 | ## Machine Learning 177 | 178 | ### Decision Trees 179 | [Decision Trees](https://mlu-explain.github.io/decision-tree/)\ 180 | [ID3, C4.5, C5.0, CART decision tree difference](https://blog.actorsfit.com/a?ID=01800-16f8ea67-422e-4850-b0b0-3749e5181112)\ 181 | [C4.5 and C5.0 Algorithm](https://en.wikipedia.org/wiki/C4.5_algorithm)\ 182 | [ID3 Algorithm](https://towardsdatascience.com/decision-trees-for-classification-id3-algorithm-explained-89df76e72df1#:~:text=the%20ID3%20algorithm%20selects%20the%20best%20feature%20at%20each%20step%20while%20building%20a%20Decision%20tree.%0ABefore%20you%20ask%2C%20the%20answer%20to%20the%20question%3A%20%E2%80%98How%20does%20ID3%20select%20the%20best%20feature%3F%E2%80%99%20is%20that%20ID3%20uses%20Information%20Gain%20or%20just%20Gain%20to%20find%20the%20best%20feature.)\ 183 | [Pruning](https://towardsdatascience.com/build-better-decision-trees-with-pruning-8f467e73b107)\ 184 | [Gini Impurity, Entropy, Classification Error](https://sebastianraschka.com/faq/docs/decision-tree-binary.html) 185 | 186 | ### Expectation Maximization (Kmeans, and GMM) 187 | [K-means clustering](https://towardsdatascience.com/k-means-clustering-algorithm-applications-evaluation-methods-and-drawbacks-aa03e644b48a)\ 188 | [Gaussian Mixture Modeling](https://towardsdatascience.com/demystifying-gaussian-mixture-models-and-expectation-maximization-a66575deaea6) 189 | 190 | ### Support Vector Machines 191 | [Support Vector Machine](https://towardsdatascience.com/understanding-support-vector-machine-part-1-lagrange-multipliers-5c24a52ffc5e)\ 192 | [SVM vs logisitic regression](https://stats.stackexchange.com/questions/270501/can-we-say-svm-and-logistic-regression-are-the-same-model-under-different-loss-f) 193 | 194 | ### Ensemble Methods 195 | [Ensemble methods: bagging, boosting and stacking](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205)\ 196 | [Adaboost](https://towardsdatascience.com/understanding-adaboost-for-decision-tree-ff8f07d2851)\ 197 | [Gradient Boosting](https://www.geeksforgeeks.org/ml-gradient-boosting/) 198 | 199 | ### Explanation Methods 200 | [Lime](https://christophm.github.io/interpretable-ml-book/lime.html)\ 201 | [Shapley and Shap](https://towardsdatascience.com/shap-shapley-additive-explanations-5a2a271ed9c3)\ 202 | [Counterfactual Explanations](https://christophm.github.io/interpretable-ml-book/counterfactual.html)\ 203 | [Global Surrogate](https://christophm.github.io/interpretable-ml-book/global.html) 204 | 205 | ### Time Series Modeling 206 | [Arima](https://medium.com/analytics-vidhya/a-thorough-introduction-to-arima-models-987a24e9ff71)\ 207 | [Sarima, Sarimax](https://towardsdatascience.com/time-series-forecasting-with-arima-sarima-and-sarimax-ee61099e78f6)\ 208 | [Prophet](https://ulvgard.se/articles/trend_and_seasonality_decomposition_with_prophet/)\ 209 | [Forecasting: Principles and Practice](https://otexts.com/fpp2/) 210 | 211 | ### Anomality Detection 212 | [General Introduction](https://medium.com/@jelkhoury880/introduction-to-anomaly-detection-methods-part-i-b1a2f389ffcb)\ 213 | [Isolation Forest](https://towardsdatascience.com/isolation-forest-the-anomaly-detection-algorithm-any-data-scientist-should-know-1a99622eec2d)\ 214 | [One Class SVM](http://rvlasveld.github.io/blog/2013/07/12/introduction-to-one-class-support-vector-machines/)\ 215 | [Local Outlier Factor](https://medium.com/towards-data-science/local-outlier-factor-for-anomaly-detection-cc0c770d2ebe)\ 216 | [Robust Covariance Estimator](https://medium.com/towards-data-science/robust-covariance-for-anomaly-detection-9c68b1ec4c4b) 217 | 218 | ### Data 219 | [Data Cleaning](https://medium.com/towards-data-science/the-ultimate-guide-to-data-cleaning-3969843991d4)\ 220 | [Imbalanced datasets](https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28)\ 221 | [Data Set Shift](https://towardsdatascience.com/understanding-dataset-shift-f2a5a262a766)\ 222 | [Covariate Shift](https://www.analyticsvidhya.com/blog/2017/07/covariate-shift-the-hidden-problem-of-real-world-data-science/#:~:text=4.%20Covariate%20Shift,and%20understand%20it.) 223 | 224 | #### Data Splitting 225 | [The Importance of Data Splitting](https://mlu-explain.github.io/train-test-validation/)\ 226 | [Training, Development and Test errors](https://docs.google.com/presentation/d/1x5MIh1ye8Q2Lc3atl_7sFXw1y1aLJqLV/edit?usp=sharing&ouid=103693764734186936730&rtpof=true&sd=true) 227 | 228 | 229 | 230 | ## Deep Learning 231 | [My sides on Convolutional Neural Networks](https://www.slideshare.net/rsanaei/convolutional-neural-networks-249580831)\ 232 | [My sides on Sequence Modles](https://www.slideshare.net/rsanaei/sequence-models-249681754) 233 | 234 | ### Transformers 235 | [Mechanics of Seq2seq Models With Attention](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention)\ 236 | [The illustrated transformer](https://jalammar.github.io/illustrated-transformer/)\ 237 | [Line-by-line implementation of “Attention is All You Need”](http://nlp.seas.harvard.edu/2018/04/03/attention.html)\ 238 | [Illustrated GPT-2](https://jalammar.github.io/illustrated-gpt2/)\ 239 | [Decoding Strategies](https://medium.com/towards-data-science/decoding-strategies-that-you-need-to-know-for-response-generation-ba95ee0faadc) 240 | 241 | --------------------------------------------------------------------------------