Billbill
size: 2.02 GB
|- W7U2_04_thank-you_bilibili.mp4 - 17.30 MB
|- W7U2_03_whats-ahead-in-the-specialization_bilibili.mp4 - 39.10 MB
|- W7U2_02_where-is-ml-going_bilibili.mp4 - 83.30 MB
|- W7U2_01_open-challenges-in-ml_bilibili.mp4 - 59.80 MB
|- W7U1_04_what-happens-after-deployment_bilibili.mp4 - 59.90 MB
|- W7U1_03_deploying-an-ml-service_bilibili.mp4 - 30.80 MB
|- W7U1_02_you-ve-made-it_bilibili.mp4 - 5.70 MB
|- W6U5_05_displaying-other-example-image-retrievals-with-a-python-lambda_bilibili.mp4 - 15.50 MB
|- W6U5_04_querying-for-the-most-similar-images-for-car-image_bilibili.mp4 - 7.30 MB
|- W6U5_03_querying-the-nearest-neighbors-model-to-retrieve-images_bilibili.mp4 - 19.70 MB
|- W6U5_02_creating-a-nearest-neighbors-model-for-image-retrieval_bilibili.mp4 - 7.20 MB
|- W6U5_01_loading-image-data_bilibili.mp4 - 12.80 MB
|- W6U4_03_training-evaluating-a-classifier-using-deep-features_bilibili.mp4 - 26.10 MB
|- W6U4_02_training-evaluating-a-classifier-using-raw-image-pixels_bilibili.mp4 - 22.80 MB
|- W6U4_01_loading-image-data_bilibili.mp4 - 15.30 MB
|- W6U3_01_deep-learning-ml-block-diagram_bilibili.mp4 - 19.50 MB
|- W6U2_06_deep-features_bilibili.mp4 - 43.70 MB
|- W6U2_05_challenges-of-deep-learning_bilibili.mp4 - 21.70 MB
|- W6U2_04_other-examples-of-deep-learning-in-computer-vision_bilibili.mp4 - 12.70 MB
|- W6U2_03_demo-of-deep-learning-model-on-imagenet-data_bilibili.mp4 - 12.20 MB
|- W6U2_02_deep-learning-performance_bilibili.mp4 - 25.00 MB
|- W6U2_01_application-of-deep-learning-to-computer-vision_bilibili.mp4 - 33.00 MB
|- W6U1_04_using-precision-recall-to-compare-recommender-models_bilibili.mp4 - 16.50 MB
|- W6U1_03_what-is-a-visual-product-recommender_bilibili.mp4 - 31.90 MB
|- W6U1_02_searching-for-images-a-case-study-in-deep-learning_bilibili.mp4 - 3.30 MB
|- W5U6_03_creating-evaluating-a-personalized-song-recommender_bilibili.mp4 - 23.80 MB
|- W5U6_02_creating-evaluating-a-popularity-based-song-recommender_bilibili.mp4 - 19.70 MB
|- W5U6_01_loading-and-exploring-song-data_bilibili.mp4 - 23.20 MB
|- W5U5_01_recommender-systems-ml-block-diagram_bilibili.mp4 - 19.00 MB
|- W5U4_03_precision-recall-curves_bilibili.mp4 - 17.70 MB
|- W5U4_02_optimal-recommenders_bilibili.mp4 - 8.30 MB
|- W5U4_01_a-performance-metric-for-recommender-systems_bilibili.mp4 - 24.50 MB
|- W5U3_05_bringing-it-all-together-featurized-matrix-factorization_bilibili.mp4 - 3.10 MB
|- W5U3_04_discovering-hidden-structure-by-matrix-factorization_bilibili.mp4 - 24.60 MB
|- W5U3_03_predictions-in-matrix-form_bilibili.mp4 - 11.30 MB
|- W5U3_02_recommendations-from-known-user-item-features_bilibili.mp4 - 19.50 MB
|- W5U3_01_the-matrix-completion-task_bilibili.mp4 - 24.00 MB
|- W5U2_03_normalizing-co-occurrence-matrices-and-leveraging-purchase-histories_bilibili.mp4 - 16.80 MB
|- W5U2_02_effect-of-popular-items_bilibili.mp4 - 5.00 MB
|- W5U2_01_collaborative-filtering-people-who-bought-this-also-bought_bilibili.mp4 - 21.70 MB
|- W5U1_04_building-a-recommender-system-via-classification_bilibili.mp4 - 15.20 MB
|- W5U1_03_where-we-see-recommender-systems-in-action_bilibili.mp4 - 2.20 MB
|- W5U1_02_recommender-systems-overview_bilibili.mp4 - 4.20 MB
|- W4U4_06_examples-of-document-retrieval-in-action_bilibili.mp4 - 20.50 MB
|- W4U4_05_building-exploring-a-nearest-neighbors-model-for-wikipedia-articles_bilibili.mp4 - 14.90 MB
|- W4U4_04_computing-distances-between-wikipedia-articles_bilibili.mp4 - 23.90 MB
|- W4U4_03_computing-exploring-tf-idfs_bilibili.mp4 - 26.90 MB
|- W4U4_02_exploring-word-counts_bilibili.mp4 - 27.10 MB
|- W4U4_01_loading-exploring-wikipedia-data_bilibili.mp4 - 25.90 MB
|- W4U3_01_clustering-and-similarity-ml-block-diagram_bilibili.mp4 - 28.10 MB
|- W4U2_04_other-examples-of-clustering_bilibili.mp4 - 34.10 MB
|- W4U2_03_k-means-a-clustering-algorithm_bilibili.mp4 - 14.60 MB
|- W4U2_02_clustering-documents-an-unsupervised-learning-task_bilibili.mp4 - 16.30 MB
|- W4U2_01_clustering-documents-task-overview_bilibili.mp4 - 19.50 MB
|- W4U1_07_retrieving-similar-documents-using-nearest-neighbor-search_bilibili.mp4 - 16.10 MB
|- W4U1_06_calculating-tf-idf-vectors_bilibili.mp4 - 21.70 MB
|- W4U1_05_prioritizing-important-words-with-tf-idf_bilibili.mp4 - 25.20 MB
|- W4U1_04_word-count-representation-for-measuring-similarity_bilibili.mp4 - 37.30 MB
|- W4U1_03_what-is-the-document-retrieval-task_bilibili.mp4 - 11.90 MB
|- W4U1_02_document-retrieval-a-case-study-in-clustering-and-measuring-similarity_bilibili.mp4 - 4.00 MB
|- W3U4_08_exploring-the-most-positive-negative-aspects-of-a-product_bilibili.mp4 - 15.70 MB
|- W3U4_07_applying-model-to-find-most-positive-negative-reviews-for-a-product_bilibili.mp4 - 14.60 MB
|- W3U4_06_evaluating-a-classifier-the-roc-curve_bilibili.mp4 - 18.40 MB
|- W3U4_05_training-a-sentiment-classifier_bilibili.mp4 - 13.10 MB
|- W3U4_04_defining-which-reviews-have-positive-or-negative-sentiment_bilibili.mp4 - 18.00 MB
|- W3U4_03_exploring-the-most-popular-product_bilibili.mp4 - 19.00 MB
|- W3U4_02_creating-the-word-count-vector_bilibili.mp4 - 9.70 MB
|- W3U4_01_loading-exploring-product-review-data_bilibili.mp4 - 14.10 MB
|- W3U3_01_classification-ml-block-diagram_bilibili.mp4 - 17.60 MB
|- W3U2_05_class-probabilities_bilibili.mp4 - 16.00 MB
|- W3U2_04_learning-curves_bilibili.mp4 - 40.10 MB
|- W3U2_03_false-positives-false-negatives-and-confusion-matrices_bilibili.mp4 - 27.40 MB
|- W3U2_02_whats-a-good-accuracy_bilibili.mp4 - 30.90 MB
|- W3U2_01_training-and-evaluating-a-classifier_bilibili.mp4 - 14.40 MB
|- W3U1_06_decision-boundaries_bilibili.mp4 - 29.30 MB
|- W3U1_05_linear-classifiers_bilibili.mp4 - 32.20 MB
|- W3U1_04_examples-of-classification-tasks_bilibili.mp4 - 25.90 MB
|- W3U1_03_what-is-an-intelligent-restaurant-review-system_bilibili.mp4 - 32.80 MB
|- W3U1_02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification_bilibili.mp4 - 5.40 MB
|- W2U4_10_applying-learned-models-to-predict-price-of-two-fancy-houses_bilibili.mp4 - 28.80 MB
|- W2U4_09_applying-learned-models-to-predict-price-of-an-average-house_bilibili.mp4 - 18.70 MB
|- W2U4_08_learning-a-model-to-predict-house-prices-from-more-features_bilibili.mp4 - 11.60 MB
|- W2U4_07_exploring-other-features-of-the-data_bilibili.mp4 - 20.90 MB
|- W2U4_06_inspecting-the-model-coefficients-learned_bilibili.mp4 - 5.40 MB
|- W2U4_05_visualizing-predictions-of-simple-model-with-matplotlib_bilibili.mp4 - 17.60 MB
|- W2U4_04_evaluating-error-rmse-of-the-simple-model_bilibili.mp4 - 9.20 MB
|- W2U4_03_learning-a-simple-regression-model-to-predict-house-prices-from-house-size_bilibili.mp4 - 10.00 MB
|- W2U4_02_splitting-the-data-into-training-and-test-sets_bilibili.mp4 - 9.80 MB
|- W2U4_01_loading-exploring-house-sale-data_bilibili.mp4 - 30.10 MB
|- W2U3_01_regression-ml-block-diagram_bilibili.mp4 - 14.70 MB
|- W2U2_04_other-regression-examples_bilibili.mp4 - 23.40 MB
|- W2U2_03_adding-other-features_bilibili.mp4 - 4.90 MB
|- W2U2_02_training-test-curves_bilibili.mp4 - 11.00 MB
|- W2U2_01_evaluating-overfitting-via-training-test-split_bilibili.mp4 - 17.70 MB
|- W2U1_05_adding-higher-order-effects_bilibili.mp4 - 16.60 MB
|- W2U1_04_linear-regression-a-model-based-approach_bilibili.mp4 - 18.90 MB
|- W2U1_03_what-is-the-goal-and-how-might-you-naively-address-it_bilibili.mp4 - 16.50 MB
|- W2U1_02_predicting-house-prices-a-case-study-in-regression_bilibili.mp4 - 6.50 MB
|- W1U4_04_using-apply-for-data-transformation_bilibili.mp4 - 19.00 MB
|- W1U4_03_interacting-with-columns-of-an-sframe_bilibili.mp4 - 16.10 MB