Path: blob/master/internal/book1/non_figures_nb_mapping_book1_urls.csv
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activation_fun_plot.ipynb,https://probml.github.io/notebooks#activation_fun_plot.ipynb softmax_plot.ipynb,https://probml.github.io/notebooks#softmax_plot.ipynb fig_2_2.ipynb,https://probml.github.io/notebooks#fig_2_2.ipynb fig_2_17.ipynb,https://probml.github.io/notebooks#fig_2_17.ipynb prob.ipynb,https://probml.github.io/notebooks#prob.ipynb gamma_dist_plot.ipynb,https://probml.github.io/notebooks#gamma_dist_plot.ipynb linreg_1d_hetero_tfp.ipynb,https://probml.github.io/notebooks#linreg_1d_hetero_tfp.ipynb student_laplace_pdf_plot.ipynb,https://probml.github.io/notebooks#student_laplace_pdf_plot.ipynb beta_dist_plot.ipynb,https://probml.github.io/notebooks#beta_dist_plot.ipynb discrete_prob_dist_plot.ipynb,https://probml.github.io/notebooks#discrete_prob_dist_plot.ipynb binom_dist_plot.ipynb,https://probml.github.io/notebooks#binom_dist_plot.ipynb bimodal_dist_plot.ipynb,https://probml.github.io/notebooks#bimodal_dist_plot.ipynb quantile_plot.ipynb,https://probml.github.io/notebooks#quantile_plot.ipynb logreg_poly_demo.ipynb,https://probml.github.io/notebooks#logreg_poly_demo.ipynb logreg_laplace_demo.ipynb,https://probml.github.io/notebooks#logreg_laplace_demo.ipynb logreg_jax.ipynb,https://probml.github.io/notebooks#logreg_jax.ipynb perceptron_demo_2d.ipynb,https://probml.github.io/notebooks#perceptron_demo_2d.ipynb logreg_iris_bayes_robust_1d_pymc3.ipynb,https://probml.github.io/notebooks#logreg_iris_bayes_robust_1d_pymc3.ipynb sigmoid_2d_plot.ipynb,https://probml.github.io/notebooks#sigmoid_2d_plot.ipynb logreg_pytorch.ipynb,https://probml.github.io/notebooks#logreg_pytorch.ipynb iris_logreg_loss_surface.ipynb,https://probml.github.io/notebooks#iris_logreg_loss_surface.ipynb logreg_sklearn.ipynb,https://probml.github.io/notebooks#logreg_sklearn.ipynb logreg_multiclass_demo.ipynb,https://probml.github.io/notebooks#logreg_multiclass_demo.ipynb 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mix_gauss_mle_vs_map.ipynb,https://probml.github.io/notebooks#mix_gauss_mle_vs_map.ipynb autodiff_pytorch.ipynb,https://probml.github.io/notebooks#autodiff_pytorch.ipynb opt_flax.ipynb,https://probml.github.io/notebooks#opt_flax.ipynb learning_rate_plot.ipynb,https://probml.github.io/notebooks#learning_rate_plot.ipynb saddle.ipynb,https://probml.github.io/notebooks#saddle.ipynb gmm_lik_surface_plot.ipynb,https://probml.github.io/notebooks#gmm_lik_surface_plot.ipynb autodiff_tf.ipynb,https://probml.github.io/notebooks#autodiff_tf.ipynb newtonsMethodNonConvex.ipynb,https://probml.github.io/notebooks#newtonsMethodNonConvex.ipynb extrema_fig_1d.ipynb,https://probml.github.io/notebooks#extrema_fig_1d.ipynb mix_gauss_demo_faithful.ipynb,https://probml.github.io/notebooks#mix_gauss_demo_faithful.ipynb lineSearchConditionNum.ipynb,https://probml.github.io/notebooks#lineSearchConditionNum.ipynb fig_8_1.ipynb,https://probml.github.io/notebooks#fig_8_1.ipynb lrschedule_tf.ipynb,https://probml.github.io/notebooks#lrschedule_tf.ipynb steepestDescentDemo.ipynb,https://probml.github.io/notebooks#steepestDescentDemo.ipynb newtonsMethodMinQuad.ipynb,https://probml.github.io/notebooks#newtonsMethodMinQuad.ipynb em_log_likelihood_max.ipynb,https://probml.github.io/notebooks#em_log_likelihood_max.ipynb matrix_factorization_recommender_surprise_lib.ipynb,https://probml.github.io/notebooks#matrix_factorization_recommender_surprise_lib.ipynb matrix_factorization_recommender.ipynb,https://probml.github.io/notebooks#matrix_factorization_recommender.ipynb linreg_sklearn.ipynb,https://probml.github.io/notebooks#linreg_sklearn.ipynb poly_regression_torch.ipynb,https://probml.github.io/notebooks#poly_regression_torch.ipynb linreg_pymc3.ipynb,https://probml.github.io/notebooks#linreg_pymc3.ipynb svi_linear_regression_1d_tfp.ipynb,https://probml.github.io/notebooks#svi_linear_regression_1d_tfp.ipynb lecun1989_with_commentary.ipynb,https://probml.github.io/notebooks#lecun1989_with_commentary.ipynb