figures_nb_mapping_book1.csv

key,url
1.3,iris_plot.ipynb
1.4,iris_dtree.ipynb
1.5,linreg_residuals_plot.ipynb
1.6,linreg_2d_surface_demo.ipynb
1.7,linreg_poly_vs_degree.ipynb
1.8,iris_kmeans.ipynb
1.9,iris_pca.ipynb
1.12,fig_1_12.ipynb
1.13,fig_1_13.ipynb
2.1,discrete_prob_dist_plot.ipynb
2.2,fig_2_2.ipynb
2.4,bimodal_dist_plot.ipynb
2.5,anscombes_quartet.ipynb
2.6,datasaurus_dozen.ipynb
2.9,binom_dist_plot.ipynb
2.10,activation_fun_plot.ipynb
2.11,iris_logreg.ipynb
2.12,softmax_plot.ipynb
2.13,iris_logreg.ipynb
2.14,linreg_1d_hetero_tfp.ipynb
2.15,student_laplace_pdf_plot.ipynb
2.16,robust_pdf_plot.ipynb
2.17,fig_2_17.ipynb
2.23,centralLimitDemo.ipynb
2.24,change_of_vars_demo1d.ipynb
3.3,simpsons_paradox.ipynb
3.5,gauss_plot_2d.ipynb
3.6,gauss_plot_2d.ipynb
3.7,gauss_imputation_known_params_demo.ipynb
3.8,gauss_infer_1d.ipynb
3.9,gauss_infer_2d.ipynb
3.10,sensor_fusion_2d.ipynb
3.11,gmm_plot_demo.ipynb
3.12,gmm_2d.ipynb
3.13,mix_bernoulli_em_mnist.ipynb
4.1,iris_cov_mat.ipynb
4.2,hinge_loss_plot.ipynb
4.3,ema_demo.ipynb
4.4,shrinkcov_plots.ipynb
4.5,linreg_poly_ridge.ipynb
4.7,polyfitRidgeCV.ipynb
4.8,imdb_mlp_bow_tf.ipynb
4.9,linreg_poly_vs_n.ipynb
4.10,beta_binom_post_plot.ipynb
4.12,beta_binom_post_pred_plot.ipynb
4.13,mixbetademo.ipynb
4.14,fig_4_14.ipynb
4.15,dirichlet_samples_plot.ipynb
4.16,gauss_infer_1d.ipynb
4.17,gauss_infer_2d.ipynb
4.18,betaHPD.ipynb
4.19,postDensityIntervals.ipynb
4.20,fig_4_20.ipynb
4.22,laplace_approx_beta_binom_jax.ipynb
4.23,bootstrapDemoBer.ipynb
4.24,samplingDistributionGaussianShrinkage.ipynb
4.25,biasVarModelComplexity3.ipynb
5.2,fig_5_2.ipynb
5.3,huberLossPlot.ipynb
5.4,coins_model_sel_demo.ipynb
5.5,linreg_eb_modelsel_vs_n.ipynb
5.6,linreg_eb_modelsel_vs_n.ipynb
5.8,riskFnGauss.ipynb
5.10,fig_5_10.ipynb
6.1,bernoulli_entropy_fig.ipynb
6.2,seq_logo_demo.ipynb
6.3,KLfwdReverseMixGauss.ipynb
6.6,MIC_correlation_2d.ipynb
7.6,gaussEvec.ipynb
7.7,height_weight_whiten_plot.ipynb
7.9,svd_image_demo.ipynb
7.10,svd_image_demo.ipynb
8.1,fig_8_1.ipynb
8.7,smooth-vs-nonsmooth-1d.ipynb
8.11,steepestDescentDemo.ipynb
8.12,lineSearchConditionNum.ipynb
8.14,fig_8_14.ipynb
8.16,lms_demo.ipynb
8.17,lrschedule_tf.ipynb
8.18,learning_rate_plot.ipynb
8.23,emLogLikelihoodMax.ipynb
8.25,mix_gauss_demo_faithful.ipynb
8.26,fig_8_26.ipynb
8.27,gmm_lik_surface_plot.ipynb
9.1,discrim_analysis_dboundaries_plot2.ipynb
9.2,discrim_analysis_dboundaries_plot2.ipynb
9.4,fisher_lda_demo.ipynb
9.5,fisher_discrim_vowel.ipynb
9.6,naive_bayes_mnist_jax.ipynb
9.7,naive_bayes_mnist_jax.ipynb
9.8,generativeVsDiscrim.ipynb
10.1,iris_logreg.ipynb
10.2,sigmoid_2d_plot.ipynb
10.4,logreg_poly_demo.ipynb
10.5,iris_logreg_loss_surface.ipynb
10.6,logreg_poly_demo.ipynb
10.7,logreg_multiclass_demo.ipynb
10.10,logreg_iris_bayes_robust_1d_pymc3.ipynb
10.13,logreg_laplace_demo.ipynb
10.14,logreg_laplace_demo.ipynb
11.1,linreg_poly_vs_degree.ipynb
11.2,linreg_contours_sse_plot.ipynb
11.4,linregOnlineDemo.ipynb
11.5,linreg_poly_vs_degree.ipynb
11.6,linreg_poly_vs_degree.ipynb
11.7,geom_ridge.ipynb
11.10,fig_11_10.ipynb
11.11,prostate_comparison.ipynb
11.12,prostate_comparison.ipynb
11.13,sparse_sensing_demo.ipynb
11.14,groupLassoDemo.ipynb
11.15,groupLassoDemo.ipynb
11.16,splines_basis_weighted.ipynb
11.17,splines_basis_heatmap.ipynb
11.18,splines_cherry_blossoms.ipynb
11.19,fig_11_19.ipynb
11.20,linreg_2d_bayes_demo.ipynb
11.21,linreg_post_pred_plot.ipynb
11.22,linreg_2d_bayes_centering_pymc3.ipynb
11.23,multi_collinear_legs_numpyro.ipynb
11.24,multi_collinear_legs_numpyro.ipynb
12.1,poisson_regression_insurance.ipynb
12.2,poisson_regression_insurance.ipynb
13.1,xor_heaviside.ipynb
13.2,activation_fun_plot.ipynb
13.4,mlp_mnist_tf.ipynb
13.6,mlp_1d_regression_hetero_tfp.ipynb
13.14,activation_fun_deriv_jax.ipynb
13.17,sparse_mlp.ipynb
13.20,sgd_minima_variance.ipynb
13.21,logregXorDemo.ipynb
13.22,linregRbfDemo.ipynb
13.23,mixexpDemoOneToMany.ipynb
14.5,conv2d_jax.ipynb
14.9,conv2d_jax.ipynb
14.17,cnn_mnist_tf.ipynb
15.2,rnn_jax.ipynb
15.17,kernel_regression_attention.ipynb
15.25,positional_encoding_jax.ipynb
16.1,knn_voronoi_plot.ipynb
16.2,knn_classify_demo.ipynb
16.3,curse_dimensionality_plot.ipynb
16.8,smoothingKernelPlot.ipynb
16.9,parzen_window_demo2.ipynb
16.10,kernelRegressionDemo.ipynb
17.1,gprDemoArd.ipynb
17.2,gpKernelPlot.ipynb
17.3,gpKernelPlot.ipynb
17.7,gprDemoNoiseFree.ipynb
17.8,gprDemoChangeHparams.ipynb
17.9,gpr_demo_marglik.ipynb
17.10,gp_classify_iris_1d_pymc3.ipynb
17.11,gp_classify_spaceflu_1d_pymc3.ipynb
17.14,svm_classifier_feature_scaling.ipynb
17.17,svm_classifier_2d.ipynb
17.18,svmCgammaDemo.ipynb
17.19,huberLossPlot.ipynb
17.20,svm_regression_1d.ipynb
17.21,kernelBinaryClassifDemo.ipynb
17.22,rvm_regression_1d.ipynb
17.23,rvm_regression_1d.ipynb
18.1,regtreeSurfaceDemo.ipynb
18.3,dtree_sensitivity.ipynb
18.4,fig_18_4.ipynb
18.5,spam_tree_ensemble_compare.ipynb
18.6,boosted_regr_trees.ipynb
18.7,hinge_loss_plot.ipynb
18.8,rf_feature_importance_mnist.ipynb
18.9,spam_tree_ensemble_interpret.ipynb
18.10,spam_tree_ensemble_interpret.ipynb
19.1,image_augmentation_jax.ipynb
19.14,hbayes_maml.ipynb
20.1,pcaDemo2d.ipynb
20.2,pca_digits.ipynb
20.3,pcaImageDemo.ipynb
20.4,pca_projected_variance.ipynb
20.5,pcaStandardization.ipynb
20.6,pcaOverfitDemo.ipynb
20.7,pcaOverfitDemo.ipynb
20.8,pcaOverfitDemo.ipynb
20.10,pcaEmStepByStep.ipynb
20.12,mixPpcaDemo.ipynb
20.13,binary_fa_demo.ipynb
20.17,ae_mnist_tf.ipynb
20.18,ae_mnist_tf.ipynb
20.19,ae_mnist_tf.ipynb
20.21,ae_mnist_tf.ipynb
20.24,fig_20_24.ipynb
20.25,fig_20_25.ipynb
20.26,fig_20_26.ipynb
20.27,vae_mnist_conv_lightning.ipynb
20.30,fig_20_30.ipynb
20.31,fig_20_31.ipynb
20.33,fig_20_33.ipynb
20.34,manifold_swiss_sklearn.ipynb
20.35,kpcaScholkopf.ipynb
20.36,fig_20_36.ipynb
20.37,fig_20_37.ipynb
20.38,fig_20_38.ipynb
20.41,fig_20_41.ipynb
21.2,agglomDemo.ipynb
21.4,hclust_yeast_demo.ipynb
21.5,yeast_data_viz.ipynb
21.6,hclust_yeast_demo.ipynb
21.7,kmeans_voronoi.ipynb
21.8,kmeans_yeast_demo.ipynb
21.9,vqDemo.ipynb
21.10,kmeans_minibatch.ipynb
21.11,fig_21_11.ipynb
21.12,kmeans_silhouette.ipynb
21.13,kmeans_silhouette.ipynb
21.14,gmm_2d.ipynb
21.15,gmm_identifiability_pymc3.ipynb
21.16,gmm_identifiability_pymc3.ipynb
21.17,gmm_chooseK_pymc3.ipynb
21.18,gmm_chooseK_pymc3.ipynb
21.19,spectral_clustering_demo.ipynb
22.3,matrix_factorization_recommender.ipynb
22.4,matrix_factorization_recommender.ipynb