深度学习最前沿技术kaggle案例实战课程深度学习之kaggle实战指南kaggle课程
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├第01课.机器学习解决问题综述课.mp4├第03课_kaggle案例实战班.mp4├第04课_kaggle案例实战班.mp4├第05课_kaggle案例实战班.mp4├第06课_kaggle案例实战班.mp4├第07课_kaggle案例实战班.mp4├第08课_kaggle案例实战班.mp4├第二节.mp4├│├││├blending.py││├cs228-python-tutorial.ipynb││├feature_engineering_and_model_tuning模型下载││├│││├││││├feature engineering.ipynb││││├test.csv││││├test_modified.csv││││├train.csv││││├train_modified.csv││││├xgboost models tuning.ipynb││││├│││││├feature engineering-checkpoint.ipynb│││││└xgboost models tuning-checkpoint.ipynb│││├││││├test.csv││││├titanic.ipynb││││├train.csv││││├│││││└titanic-checkpoint.ipynb│││├││││├kaggle_bicycle_example.ipynb││││├kaggle_bike_competition_train.csv││││├│││││└kaggle_bicycle_example-checkpoint.ipynb││││├│││││├kaggle_bicycle_example_34_0.png│││││├kaggle_bicycle_example_42_0.png│││││├kaggle_bicycle_example_43_0.png│││││├kaggle_bicycle_example_44_0.png│││││├kaggle_bicycle_example_45_0.png│││││├kaggle_bicycle_example_46_1.png│││││├kaggle_bicycle_example_47_1.png│││││└kaggle_bicycle_example_49_1.png│├││├│││├data_description模型下载│││├│││├││││├sample_submission.csv││││├test.csv││││└train.csv│││├││││├house_price.html││││├house_price.ipynb││││├house_price_advanced.html││││├house_price_advanced.ipynb││││├│││││├house_price_advanced-checkpoint.ipynb│││││└house_price-checkpoint.ipynb││├│││├│││├││││├combined_news_djia.csv││││├djia_table.csv││││└redditnews.csv│││├││││├news_stock.html││││├news_stock.ipynb││││├│││││└news_stock-checkpoint.ipynb│├││├a危azu-ctr-prediction-lr模型下载││├feature.search││├feature.search_ads││├feature_map.search_ads││├generate_train_feature_mapper.py││├generate_train_feature_reducer.py││├kaggle-a危azu-rank1模型下载││├kaggle-a危azu-rank2模型下载││├search_ads_feature.sample││├search_click_data.sample││├spark-criteo-ctr-prediction.ipynb││└xgb_ads.conf│├││├││├│││├news_stock.html│││├news_stock_advanced.html│││├search relevance.ipynb│││├search relevance_advanced.ipynb│││├search+relevance.html│││├search+relevance_advanced.html│││├││││├search relevance_advanced-checkpoint.ipynb││││└search relevance-checkpoint.ipynb│├││├energy_forecasting_notebooks模型下载││└subway_prediction_notebook模型下载│├││├cat_dog.html││├char_rnn.html││├image_search.html││├kaggle第06课：走起~深度学习.pdf││├kaggle第06课：走起~深度学习.pptx││├news_stock_advanced.html││├word_rnn.html││├│││├chi_square.png│││└rgbhistogram.jpg││├│││├cats-vs-dogs模型下载│││├sample_submission.csv│││├test模型下载│││└train模型下载│├││├data模型下载││├kaggle event recommendation competition.ipynb││├kaggle-event-recommendation-rank1模型下载││└rossmann_store_sales_competition.ipynb│├││└ppd_riskcontrol_competition模型下载├│├kaggle第05课：能源预测与分配问题.pdf│├kaggle第06课：走起~深度学习.pdf│├kaggle第06课：走起~深度学习.pptx│├││├kaggle第01课：机器学习算法、工具与流程概述.pdf││└分享的链接模型下载│├││└kaggle第02课：经济金融相关问题.pdf│├││├kaggle-2014-criteo.pdf││├kaggle-a危azu.pdf││├predicting-clicks-facebook.pdf││├阿里妈妈：大数据下的广告排序技术及实践.pdf││├百度凤巢：dnn在凤巢ctr预估中的应用.pdf││├从fm到ffm.pdf││├第3课–排序与ctr预估.pdf││├京东电商广告和推荐系统的机器学习系统实践.pdf││└腾讯广点通：效果广告中的机器学习技术.pdf│├││└kaggle第四课.pdf│├││└第5课：能源预测与分配问题.pdf│├││└第7课：推荐与销量预测相关问题.pdf│├││├第8课：金融风控问题.pdf││└金融风控大赛解决方案.pdf
