Updated Jun 10, 2026

DOUGE

Projects, writing, collected links, and small records from building things. Browse by time when you want chronology, or by tags when you want context.

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W Case Study (1) — Feature Engineering Preface: machine learning engineers spend roughly half their time on data cleaning, feature selection, dimensionality reduction, and other data processing. This article uses an email filtering system as an example to introduce important work before building a machine learning model. W Machine Learning (20) — Dimensionality Reduction Why reduce dimensions, and main methods: PCA, LDA, and topic models. W Machine Learning (19) — Feature Engineering Preface: feature engineering is central to machine learning and directly affects model quality. W Machine Learning (16) — EM Algorithm Maximum likelihood with latent variables, K-Means as EM intuition, Jensen's inequality, and a GMM training example. W Machine Learning (17) — GMM Algorithm Gaussian mixture models, EM steps for GMM, and height/weight classification example with sklearn. W Machine Learning (13) — AdaBoost Boosting ensemble learning, AdaBoost algorithm, sample and classifier weights, and comparison with Bagging. W Machine Learning (14) — Naive Bayes Bayesian probability, naive Bayes variants, text classification, and classifier comparison on news data. W Machine Learning (15) — Bayesian Networks Bayesian networks for correlated features, conditional independence, and comparison with naive Bayes. W Machine Learning (12) — Random Forest Ensemble learning, random forest construction, and a cervical cancer risk prediction example. W Running Notes — April April running, films, and Qingming — Year of the Dog, My Native Heath, Manchester by the Sea, and Summer with Kikujiro.
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W Machine Learning (10) — Linear SVM Support vector machines: linearly separable SVM, margin, support vectors, dual formulation, and algorithm steps. W Machine Learning (11) — Nonlinear SVM Soft margin, polynomial feature expansion, mapping functions, and kernel methods for nonlinear SVM. W Machine Learning (9) — SVM Mathematical Foundations Optimization, Lagrange multipliers, KKT conditions, distance to hyperplanes, and the perceptron model for SVM. W Machine Learning (8) — Other Clustering Methods Hierarchical clustering, agglomerative and divisive methods, inter-cluster distance, and BIRCH. W Machine Learning (7) — Clustering Algorithms Unsupervised clustering: distance metrics, K-Means, improvements, density clustering, and evaluation. W Machine Learning (6) — Decision Trees Preface: decision trees build a tree model first, then derive the loss function as a metric; information entropy, pruning, and visualization. W Machine Learning (5) — KNN K-nearest neighbors and KD-Tree: algorithm principles, the three key elements, and implementation. W Machine Learning (5) — Logistic Regression Logistic regression and softmax for classification: sigmoid, cross-entropy loss, and gradient descent. W Machine Learning (3) — Regression Models Polynomial expansion, overfitting, regularization (L1/L2), Ridge and LASSO regression, and hyperparameter tuning. W Machine Learning (3) — Least Squares Least squares concept, Gaussian error assumptions, household power example, and R²/MSE metrics. W Running Notes — March March running diary—Lantern Festival moon viewing, Xiangling learning poetry, Wang Guowei on aesthetic realms, and 'A crane's shadow crosses the cold pond.'
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W From List to Tree: A Personal Site Information Structure Why a personal site can feel more like a living index. W My Best Resume Material Was Hidden in 700 Conversations A note on memory, prompts, and practical self-knowledge. W Why You Should Come to Hatch A field note about builders and unusually dense rooms. W The Ignored Continent Reading notes on attention, maps, and inherited blind spots. W Three Hundred Strangers A short essay about brief encounters and durable memory. W Running Restart — 2020 Picking up running again after more than two years: insomnia, shortness of breath, weaker self-discipline, and the return of a good feeling after five laps around the park. W Reading Beauty in Mathematics 01 A first read that felt like a revelation—and Wu Jun on education, interest, and gifted-youth programs. W Analyzing Dream of the Red Chamber Character Relations with Word Vectors Can machines read the love between Baoyu and Daiyu? n-gram and Word2Vec analysis of character word vectors in Honglou. W Running Notes — April April running, films, and Qingming — Year of the Dog, My Native Heath, Manchester by the Sea, and Summer with Kikujiro. W Running Notes — March March running diary—Lantern Festival moon viewing, Xiangling learning poetry, Wang Guowei on aesthetic realms, and 'A crane's shadow crosses the cold pond.' W Running Notes (2) February running notes—math and inner product spaces, Crouching Tiger Hidden Dragon, hometown, Lunar New Year, and the last day of February. W A Few Reading Programs I Recommend Liang Wendao, Ma Ruifang, Bi Feiyu, Jiang Xun—programs that stayed with me for a long time, compiled on a snowy day. W Diary — First Snow A childhood weekly essay on snow earned an A—the last praise I ever got in the humanities; today it snows again, and I have never hated a snowy day more. W Confucian Culture Mind Map A mind map of Confucian culture notes, originally from Baidu Brain Map. W Miguel Street — Does My Sadness Make You Any Happier Miguel Street through the eyes of an innocent narrator—artists, the poet B. Wordsworth, and the house reduced to brick and cement. W Kite in the Rice Field An old man on Ox Tooth Hill, forty years of solitude, and notebook entries of dreams and reality. (To be continued) W The Pumpkin Adventure In the Flower Kingdom, Jasmine Mother and her three children, Uncle Pumpkin's bottle of potion, and the peony that appeared afterward. W A Circle on the Subway From ants by the village bridge to the crowds on the Shanghai subway—familiar smallness and an unstoppable push forward. W Brief Notes on Dreams in Dream of the Red Chamber Rereading in solitude—Baoyu's mirror dream and Daiyu's nightmare, truth and illusion inverted. W My Guitar Story Bored in senior year, a free guitar lesson post on the forum led to a wedding-host teacher, a yellow guitar, and a story that soon fizzled out. W Bumping Into an Old Lady Rushing along, I collided with an elderly woman working by the road—and was pulled into hospitals, scans, and a string of uneasy uncertainties.