吴恩达机器学习的python-code.rar

[{"title":"（ 82 个子文件 31.2MB ） 吴恩达机器学习的python-code.rar","children":[{"title":"吴恩达机器学习的python-code","children":[{"title":"ex1-linear regression","children":[{"title":"ex1.pdf <span style='color:#111;'> 478.45KB </span>","children":null,"spread":false},{"title":"1.linear_regreesion_v1.ipynb <span style='color:#111;'> 261.10KB </span>","children":null,"spread":false},{"title":"ex1data2.txt <span style='color:#111;'> 704B </span>","children":null,"spread":false},{"title":"ex1data1.txt <span style='color:#111;'> 1.42KB </span>","children":null,"spread":false},{"title":"ML-Exercise1.ipynb <span style='color:#111;'> 138.61KB </span>","children":null,"spread":false}],"spread":true},{"title":"ex2-logistic regression","children":[{"title":"ML-Exercise2.ipynb <span style='color:#111;'> 101.02KB </span>","children":null,"spread":false},{"title":"1. logistic_regression_v1.ipynb <span style='color:#111;'> 295.92KB </span>","children":null,"spread":false},{"title":"ex2.pdf <span style='color:#111;'> 228.18KB </span>","children":null,"spread":false},{"title":"ex2data1.txt <span style='color:#111;'> 3.78KB </span>","children":null,"spread":false},{"title":"ex2data2.txt <span style='color:#111;'> 2.30KB </span>","children":null,"spread":false}],"spread":true},{"title":"ex6-SVM","children":[{"title":"data","children":[{"title":"ex6data2.mat <span style='color:#111;'> 7.43KB </span>","children":null,"spread":false},{"title":"spamSample2.txt <span style='color:#111;'> 253B </span>","children":null,"spread":false},{"title":"emailSample1.txt <span style='color:#111;'> 403B </span>","children":null,"spread":false},{"title":"ex6data3.mat <span style='color:#111;'> 5.90KB </span>","children":null,"spread":false},{"title":"emailSample2.txt <span style='color:#111;'> 1.30KB </span>","children":null,"spread":false},{"title":"vocab.txt <span style='color:#111;'> 21.62KB </span>","children":null,"spread":false},{"title":"spamSample1.txt <span style='color:#111;'> 697B </span>","children":null,"spread":false},{"title":"spamTrain.mat <span style='color:#111;'> 418.76KB </span>","children":null,"spread":false},{"title":"spamTest.mat <span style='color:#111;'> 110.08KB </span>","children":null,"spread":false},{"title":"ex6data1.mat <span style='color:#111;'> 981B </span>","children":null,"spread":false}],"spread":true},{"title":"4- spam filter.ipynb <span style='color:#111;'> 5.91KB </span>","children":null,"spread":false},{"title":"ex6.pdf <span style='color:#111;'> 327.78KB </span>","children":null,"spread":false},{"title":"1- linear SVM.ipynb <span style='color:#111;'> 77.44KB </span>","children":null,"spread":false},{"title":"2- Gaussian kernels.ipynb <span style='color:#111;'> 140.71KB </span>","children":null,"spread":false},{"title":"3- search for the best parameters.ipynb <span style='color:#111;'> 13.22KB </span>","children":null,"spread":false},{"title":"ML-Exercise6.ipynb <span style='color:#111;'> 179.60KB </span>","children":null,"spread":false}],"spread":true},{"title":"ex3-neural network","children":[{"title":"ex3weights.mat <span style='color:#111;'> 77.73KB </span>","children":null,"spread":false},{"title":"ex3.pdf <span style='color:#111;'> 286.82KB </span>","children":null,"spread":false},{"title":"1- neural network.ipynb <span style='color:#111;'> 90.73KB </span>","children":null,"spread":false},{"title":"ex3data1.mat <span style='color:#111;'> 7.16MB </span>","children":null,"spread":false},{"title":"ML-Exercise3.ipynb <span style='color:#111;'> 16.25KB </span>","children":null,"spread":false}],"spread":true},{"title":"ex7-kmeans and PCA","children":[{"title":"4- 2D PCA.ipynb <span style='color:#111;'> 67.02KB </span>","children":null,"spread":false},{"title":"data","children":[{"title":"bird_small.mat <span style='color:#111;'> 44.54KB </span>","children":null,"spread":false},{"title":"ex7data1.mat <span style='color:#111;'> 995B </span>","children":null,"spread":false},{"title":"ex7faces.mat <span style='color:#111;'> 10.52MB </span>","children":null,"spread":false},{"title":"bird_small.png <span style='color:#111;'> 32.26KB </span>","children":null,"spread":false},{"title":"ex7data2.mat <span style='color:#111;'> 4.67KB </span>","children":null,"spread":false}],"spread":true},{"title":"3- kmeans for image compression.ipynb <span style='color:#111;'> 300.83KB </span>","children":null,"spread":false},{"title":"2- 2D kmeans.ipynb <span style='color:#111;'> 160.17KB </span>","children":null,"spread":false},{"title":"5- PCA on face data.ipynb <span style='color:#111;'> 679.49KB </span>","children":null,"spread":false},{"title":"ML-Exercise7.ipynb <span style='color:#111;'> 387.80KB </span>","children":null,"spread":false},{"title":"ex7.pdf <span style='color:#111;'> 699.16KB </span>","children":null,"spread":false},{"title":"1- visualize data.ipynb <span style='color:#111;'> 45.95KB </span>","children":null,"spread":false}],"spread":true},{"title":"ex5-bias vs variance","children":[{"title":"ML-Exercise5.ipynb <span style='color:#111;'> 156.56KB </span>","children":null,"spread":false},{"title":"ex5data1.mat <span style='color:#111;'> 1.29KB </span>","children":null,"spread":false},{"title":"ex5.pdf <span style='color:#111;'> 182.97KB </span>","children":null,"spread":false}],"spread":true},{"title":"README.md <span style='color:#111;'> 215B </span>","children":null,"spread":false},{"title":"ex8-anomaly detection and recommendation","children":[{"title":"data","children":[{"title":"ex8data2.mat <span style='color:#111;'> 91.29KB </span>","children":null,"spread":false},{"title":"ex8data1.mat <span style='color:#111;'> 9.28KB </span>","children":null,"spread":false},{"title":"movie_ids.txt <span style='color:#111;'> 48.95KB </span>","children":null,"spread":false},{"title":"ex8_movieParams.mat <span style='color:#111;'> 196.48KB </span>","children":null,"spread":false},{"title":"ex8_movies.mat <span style='color:#111;'> 218.16KB </span>","children":null,"spread":false}],"spread":true},{"title":"ML-Exercise8.ipynb <span style='color:#111;'> 154.11KB </span>","children":null,"spread":false},{"title":"ex8.pdf <span style='color:#111;'> 242.36KB </span>","children":null,"spread":false},{"title":"2- Recommender system.ipynb <span style='color:#111;'> 16.59KB </span>","children":null,"spread":false},{"title":"1- Anomaly detection.ipynb <span style='color:#111;'> 70.29KB </span>","children":null,"spread":false}],"spread":true},{"title":"img","children":[{"title":"logistic_gradient.png <span style='color:#111;'> 15.27KB </span>","children":null,"spread":false},{"title":"logistic_cost.png <span style='color:#111;'> 19.50KB </span>","children":null,"spread":false},{"title":"mapped_feature.png <span style='color:#111;'> 6.50KB </span>","children":null,"spread":false},{"title":"nn_cost.png <span style='color:#111;'> 19.68KB </span>","children":null,"spread":false},{"title":"reg_cost.png <span style='color:#111;'> 7.11KB </span>","children":null,"spread":false},{"title":"linear_reg_cost.png <span style='color:#111;'> 8.52KB </span>","children":null,"spread":false},{"title":"nn_model.png <span style='color:#111;'> 119.33KB </span>","children":null,"spread":false},{"title":"pca_projection.png <span style='color:#111;'> 22.59KB </span>","children":null,"spread":false},{"title":"gradient_checking.png <span style='color:#111;'> 8.44KB </span>","children":null,"spread":false},{"title":"rcmd_vectorized_grad.png <span style='color:#111;'> 5.20KB </span>","children":null,"spread":false},{"title":"rcmd_gradient.png <span style='color:#111;'> 13.45KB </span>","children":null,"spread":false},{"title":"rcmd_cost.png <span style='color:#111;'> 17.51KB </span>","children":null,"spread":false},{"title":"cov_mat.png <span style='color:#111;'> 2.36KB </span>","children":null,"spread":false},{"title":"reg_gradient.png <span style='color:#111;'> 8.19KB </span>","children":null,"spread":false},{"title":"linear_gradient.png <span style='color:#111;'> 3.91KB </span>","children":null,"spread":false},{"title":"nn_reg_grad.png <span style='color:#111;'> 26.66KB </span>","children":null,"spread":false},{"title":"f1_score.png <span style='color:#111;'> 49.28KB </span>","children":null,"spread":false},{"title":"linear_cost.png <span style='color:#111;'> 5.32KB </span>","children":null,"spread":false},{"title":"nn_regcost.png <span style='color:#111;'> 35.36KB </span>","children":null,"spread":false},{"title":"linear_reg_gradient.png <span style='color:#111;'> 18.56KB </span>","children":null,"spread":false},{"title":"rcmd_reg_grad.png <span style='color:#111;'> 14.67KB </span>","children":null,"spread":false}],"spread":false},{"title":"ex4-NN back propagation","children":[{"title":"ex4weights.mat <span style='color:#111;'> 77.73KB </span>","children":null,"spread":false},{"title":"1- NN back propagation.ipynb <span style='color:#111;'> 194.93KB </span>","children":null,"spread":false},{"title":"ex4.pdf <span style='color:#111;'> 348.33KB </span>","children":null,"spread":false},{"title":"ex4data1.mat <span style='color:#111;'> 7.16MB </span>","children":null,"spread":false},{"title":"ML-Exercise4.ipynb <span style='color:#111;'> 23.23KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}]