├── .ipynb_checkpoints ├── 01_the_machine_learning_landscape-checkpoint.ipynb ├── 17_Autoencoder_and_GANS-checkpoint.ipynb └── 18_Reinforcement_Learning-checkpoint.ipynb ├── 01_the_machine_learning_landscape.ipynb ├── 02_end_to_end_machine_learning_project.ipynb ├── 04_Training_Models.ipynb ├── 05_Support Vector Machines.ipynb ├── 06_Decision_Trees.ipynb ├── 10_Introduction to Artificial Neural Networks with Keras.ipynb ├── 11_Training_Deep_Neural_Networks.ipynb ├── 12_Custom_Models_and_Training_with_TensorFlow.ipynb ├── 17_Autoencoder_and_GANS.ipynb ├── 18_Reinforcement_Learning.ipynb ├── 19_Training and Deploying TensorFlow Models at Scale.ipynb ├── 7. Ensemble Learning and Random Forests-集合学习和随机森林.ipynb ├── LICENSE ├── README.md ├── datasets ├── housing │ ├── README.md │ ├── housing.csv │ └── housing.tgz ├── inception │ └── imagenet_class_names.txt ├── jsb_chorales │ ├── README.md │ └── jsb_chorales.tgz └── lifesat │ ├── README.md │ ├── gdp_per_capita.csv │ ├── lifesat.csv │ └── oecd_bli_2015.csv ├── images ├── 123 ├── 02_End_to_End_project │ ├── california.png │ ├── california_housing_prices_plot.png │ ├── housing_prices_scatterplot.png │ ├── income_vs_house_value_scatterplot.png │ └── scatter_matrix_plot.png ├── 03_Classification_MNIST │ ├── cleaned_digit_example_plot.png │ ├── confusion_matrix_errors_plot.png │ ├── confusion_matrix_plot.png │ ├── error_analysis_digits_plot.png │ ├── more_digits_plot.png │ ├── noisy_digit_example_plot.png │ ├── precision_recall_vs_threshold_plot.png │ ├── precision_vs_recall_plot.png │ ├── roc_curve_comparison_plot.png │ └── roc_curve_plot.png ├── 04_Training Models │ ├── early_stopping_plot.png │ ├── generated_data_plot.png │ ├── gradient_descent_paths_plot.png │ ├── gradient_descent_plot.png │ ├── high_degree_polynomials_plot.png │ ├── lasso_regression_plot.png │ ├── lasso_vs_ridge_plot.png │ ├── learning_curves_plot.png │ ├── logistic_function_plot.png │ ├── logistic_regression_contour_plot.png │ ├── logistic_regression_plot.png │ ├── quadratic_data_plot.png │ ├── quadratic_predictions_plot.png │ ├── ridge_regression_plot.png │ ├── sgd_plot.png │ ├── softmax_regression_contour_plot.png │ └── underfitting_learning_curves_plot.png ├── 05_Support Vector Machines │ ├── higher_dimensions_plot.png │ ├── hinge_plot.png │ ├── iris_3D_plot.png │ ├── kernel_method_plot.png │ ├── large_margin_classification_plot.png │ ├── moons_kernelized_polynomial_svc_plot.png │ ├── moons_polynomial_svc_plot.png │ ├── moons_rbf_svc_plot.png │ ├── regularization_plot.png │ ├── small_w_large_margin_plot.png │ ├── svm_regression_plot.png │ └── svm_with_polynomial_kernel_plot.png ├── 06_Decision Trees │ ├── A Decision Tree for regression.png │ ├── decision_tree_decision_boundaries_plot.png │ ├── decision_tree_instability_plot.png │ ├── iris_tree.dot │ ├── iris_tree.png │ ├── min_samples_leaf_plot.png │ ├── regression_tree.dot │ ├── regression_tree.png │ ├── sensitivity_to_rotation_plot.png │ ├── tree_regression_plot.png │ └── tree_regression_regularization_plot.png ├── 07_Ensemble Learning and Random Forests │ ├── boosting_plot.png │ ├── decision_tree_without_and_with_bagging_plot.png │ ├── early_stopping_gbrt_plot.png │ ├── gbrt_learning_rate_plot.png │ ├── gradient_boosting_plot.png │ ├── law_of_large_numbers_plot.png │ └── mnist_feature_importance_plot.png ├── 08_ Dimensionality Reduction -- unsupervised_learning │ ├── aic_bic_vs_k_diagram.png │ ├── bad_kmeans_diagram.png │ ├── bad_n_clusters_diagram.png │ ├── blobs_diagram.png │ ├── classification_vs_clustering_diagram.png │ ├── cluster_classification_diagram.png │ ├── covariance_type_diagram.png │ ├── dataset_2d_plot.png │ ├── dataset_3d_plot.png │ ├── dbscan_diagram.png │ ├── gaussian_mixtures_diagram.png │ ├── image_segmentation_diagram.png │ ├── inertia_vs_k_diagram.png │ ├── kernel_pca_plot.png │ ├── kmeans_algorithm_diagram.png │ ├── kmeans_variability_diagram.png │ ├── ladybug.png │ ├── likelihood_function_diagram.png │ ├── lle_unrolling_plot.png │ ├── manifold_decision_boundary_plot1.png │ ├── manifold_decision_boundary_plot2.png │ ├── manifold_decision_boundary_plot3.png │ ├── manifold_decision_boundary_plot4.png │ ├── minibatch_kmeans_vs_kmeans.png │ ├── mixture_anomaly_detection_diagram.png │ ├── mixture_concentration_prior_diagram.png │ ├── mnist_compression_plot.png │ ├── moons_vs_bgm_diagram.png │ ├── other_dim_reduction_plot.png │ ├── pca_best_projection.png │ ├── preimage_plot.png │ ├── representative_images_diagram.png │ ├── silhouette_analysis_diagram.png │ ├── silhouette_score_vs_k_diagram.png │ ├── squished_swiss_roll_plot.png │ ├── swiss_roll_plot.png │ └── voronoi_diagram.png ├── 10_Introduction to Artificial Neural Networks │ ├── activation_functions_plot.png │ └── perceptron_iris_plot.png ├── 11_Training Deep Neural Nets │ ├── elu_plot.png │ ├── leaky_relu_plot.png │ ├── selu_plot.png │ └── sigmoid_saturation_plot.png ├── 13_CNN │ ├── README │ ├── china_horizontal.png │ ├── china_max_pool.png │ ├── china_original.png │ ├── china_vertical.png │ └── test_image.png ├── 14_RNN │ ├── creative_sequence_plot.png │ ├── time_series_plot.png │ └── time_series_pred_plot.png ├── 15_autoencoders │ ├── extracted_features_plot.png │ ├── generated_digits_plot.png │ ├── linear_autoencoder_pca_plot.png │ ├── reconstruction_plot.png │ └── sparsity_loss_plot.png ├── 16_Reinforcement Learning │ └── cart_pole_plot.png └── autoencoders │ └── dimension reduction.png └── my_model.pkl /.ipynb_checkpoints/01_the_machine_learning_landscape-checkpoint.ipynb: -------------------------------------------------------------------------------- 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