├── Course 2 of 7: How to Win a Data Science Competition: Learn from Top Kagglers ├── Final project │ ├── Documentation.pdf │ ├── data_processing.ipynb │ ├── hyperopt_xgb.ipynb │ ├── training_xgboost.ipynb │ └── xgb.pickle.dat ├── INSTALLED_PACKAGES.ipynb ├── Programming assignment, week 1: Pandas basics │ ├── .ipynb_checkpoints │ │ └── PandasBasics-checkpoint.ipynb │ ├── PandasBasics.ipynb │ ├── __pycache__ │ │ └── grader.cpython-36.pyc │ └── grader.py ├── Programming assignment, week 2: Data leakages │ ├── .ipynb_checkpoints │ │ └── Data leakages-checkpoint.ipynb │ ├── Data leakages.ipynb │ ├── submission.csv │ └── submit.csv ├── Programming assignment, week 3: Mean encodings │ ├── .ipynb_checkpoints │ │ └── Programming_assignment_week_3-checkpoint.ipynb │ ├── Programming_assignment_week_3.ipynb │ ├── __pycache__ │ │ └── grader.cpython-36.pyc │ └── grader.py ├── Programming assignment, week 4: Ensembles │ ├── .ipynb_checkpoints │ │ └── Programming_assignment_week_4-checkpoint.ipynb │ ├── Programming_assignment_week_4.ipynb │ ├── __pycache__ │ │ └── grader.cpython-36.pyc │ └── grader.py ├── Programming assignment, week 4: KNN features │ ├── .ipynb_checkpoints │ │ ├── Untitled-checkpoint.ipynb │ │ ├── Untitled1-checkpoint.ipynb │ │ ├── Untitled2-checkpoint.ipynb │ │ └── compute_KNN_features-checkpoint.ipynb │ ├── Untitled.ipynb │ ├── Untitled1.ipynb │ ├── Untitled2.ipynb │ ├── __pycache__ │ │ └── grader.cpython-36.pyc │ ├── compute_KNN_features.ipynb │ ├── data │ │ ├── knn_feats_cosine_test.npy │ │ ├── knn_feats_cosine_train.npy │ │ ├── knn_feats_minkowski_test.npy │ │ └── knn_feats_minkowski_train.npy │ └── grader.py ├── README.md ├── Reading materials │ ├── .ipynb_checkpoints │ │ └── GBM_drop_tree-checkpoint.ipynb │ ├── EDA_Springleaf_screencast.ipynb │ ├── EDA_video2.ipynb │ ├── EDA_video3_screencast.ipynb │ ├── GBM_drop_tree.ipynb │ ├── Hyperparameters_tuning_video2_RF_n_estimators.ipynb │ ├── Macros.ipynb │ ├── Metrics_video2_constants_for_MSE_and_MAE.ipynb │ ├── Metrics_video3_weighted_median.ipynb │ └── Metrics_video8_soft_kappa_xgboost.ipynb ├── Untitled.ipynb ├── currdir.tar └── readonly │ ├── KNN_features_data │ ├── X.npz │ ├── X_test.npz │ ├── Y.npy │ ├── Y_test.npy │ └── knn_feats_test_first50.npy │ ├── data_leakages_data │ └── test_pairs.csv │ ├── final_project_data │ ├── item_categories.csv │ ├── items.csv │ ├── sales_train.csv.gz │ ├── sample_submission.csv │ ├── shops.csv │ └── test.csv │ └── reading_materials │ ├── EDA_Springleaf_screencast.ipynb │ ├── EDA_video2.ipynb │ ├── EDA_video3_screencast.ipynb │ ├── Hyperparameters_tuning_video2_RF_n_estimators.ipynb │ ├── Macros.ipynb │ ├── Metrics_video2_constants_for_MSE_and_MAE.ipynb │ ├── Metrics_video3_weighted_median.ipynb │ └── Metrics_video8_soft_kappa_xgboost.ipynb └── Course 1 of 7: Introduction to Deep Learning ├── week1 ├── .ipynb_checkpoints │ └── week01_pa-checkpoint.ipynb ├── kernel.png ├── sgd.png ├── target.npy ├── train.npy └── week01_pa.ipynb ├── week2 ├── .ipynb_checkpoints │ ├── Keras-task-checkpoint.ipynb │ ├── NumpyNN (honor)-checkpoint.ipynb │ ├── Peer_homework-checkpoint.ipynb │ ├── Peer_homework_2-checkpoint.ipynb │ ├── Peer_homework_3-checkpoint.ipynb │ ├── Sample Solution-checkpoint.ipynb │ ├── Tensorflow-task-checkpoint.ipynb │ └── my1stNN-checkpoint.ipynb ├── Keras-task.ipynb ├── NumpyNN (honor).ipynb ├── Peer_homework.ipynb ├── Peer_homework_2.ipynb ├── Peer_homework_3.ipynb ├── Sample Solution.ipynb ├── Tensorflow-task.ipynb ├── __pycache__ │ ├── matplotlib_utils.cpython-36.pyc │ ├── preprocessed_mnist.cpython-36.pyc │ ├── submit.cpython-36.pyc │ └── util.cpython-36.pyc ├── matplotlib_utils.py ├── my1stNN.ipynb ├── preprocessed_mnist.py ├── submit.py ├── util.py ├── validation_predictons.txt └── weights.h5 ├── week3 ├── .ipynb_checkpoints │ ├── week3_task1_first_cnn_cifar10_clean-checkpoint.ipynb │ └── week3_task2_fine_tuning_clean-checkpoint.ipynb ├── 102flowers.tgz ├── __pycache__ │ └── grading_utils.cpython-36.pyc ├── grading_utils.py ├── imagelabels.mat ├── images │ ├── center_crop.jpg │ ├── cifar10.jpg │ ├── flowers.jpg │ └── inceptionv3.png ├── week3_task1_first_cnn_cifar10_clean.ipynb ├── week3_task2_fine_tuning_clean.ipynb └── weights.h5 ├── week4 ├── .ipynb_checkpoints │ ├── Adversarial-task-checkpoint.ipynb │ └── Autoencoders-task-checkpoint.ipynb ├── Adversarial-task.ipynb ├── Autoencoders-task.ipynb ├── __pycache__ │ └── lfw_dataset.cpython-36.pyc ├── images │ ├── autoencoder.png │ ├── denoising.jpg │ ├── pca.png │ ├── similar_images.jpg │ └── transpose_conv.jpg ├── lfw-deepfunneled.tgz ├── lfw.tgz ├── lfw_attributes.txt ├── lfw_dataset.py ├── submit.py └── submit_honor.py ├── week5 ├── .ipynb_checkpoints │ └── RNN-task-checkpoint.ipynb ├── POS-task.ipynb ├── RNN-task.ipynb ├── __pycache__ │ └── submit.cpython-36.pyc ├── data_copyright ├── mtg_card_names.txt ├── names ├── rnn.png └── submit.py └── week6 ├── grading_utils.py ├── images ├── encoder_decoder.png ├── 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