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
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===============课程目录===============
├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
├<代码>
│├<lecture01>
││├blending.py
││├cs228-python-tutorial.ipynb
││├Feature_engineering_and_model_tuning.zip
││├<Feature_engineering_and_model_tuning>
│││├<Feature-engineering_and_Parameter_Tuning_XGBoost>
││││├Feature Engineering.ipynb
││││├Test.csv
││││├test_modified.csv
││││├Train.csv
││││├train_modified.csv
││││├XGBoost models tuning.ipynb
││││├<.ipynb_checkpoints>
│││││├Feature Engineering-checkpoint.ipynb
│││││└XGBoost models tuning-checkpoint.ipynb
│││├<Kaggle_Titanic>
││││├test.csv
││││├Titanic.ipynb
││││├train.csv
││││├<.ipynb_checkpoints>
│││││└Titanic-checkpoint.ipynb
│││├<Kaggle-Bicycle-Example>
││││├Kaggle_Bicycle_Example.ipynb
││││├kaggle_bike_competition_train.csv
││││├<.ipynb_checkpoints>
│││││└Kaggle_Bicycle_Example-checkpoint.ipynb
││││├<Kaggle_Bicycle_Example_files>
│││││├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
│├<lecture02>
││├<house price>
│││├data_description.txt
│││├<_ipynb_checkpoints>
│││├<input>
││││├sample_submission.csv
││││├test.csv
││││└train.csv
│││├<notebook>
││││├house_price.html
││││├house_price.ipynb
││││├house_price_advanced.html
││││├house_price_advanced.ipynb
││││├<.ipynb_checkpoints>
│││││├house_price_advanced-checkpoint.ipynb
│││││└house_price-checkpoint.ipynb
││├<news stock>
│││├<_ipynb_checkpoints>
│││├<input>
││││├Combined_News_DJIA.csv
││││├DJIA_table.csv
││││└RedditNews.csv
│││├<notebook>
││││├news_stock.html
││││├news_stock.ipynb
││││├<.ipynb_checkpoints>
│││││└news_stock-checkpoint.ipynb
│├<lecture03>
││├avazu-CTR-Prediction-LR.zip
││├feature.search
││├feature.search_ads
││├feature_map.search_ads
││├generate_train_feature_mapper.py
││├generate_train_feature_reducer.py
││├kaggle-avazu-rank1.zip
││├kaggle-avazu-rank2.zip
││├search_ads_feature.sample
││├search_click_data.sample
││├Spark-Criteo-CTR-Prediction.ipynb
││└xgb_ads.conf
│├<lecture04>
││├<input数据太大。就不传了。自己下载吧~ - 老师留>
││├<notebook>
│││├news_stock.html
│││├news_stock_advanced.html
│││├search relevance.ipynb
│││├search relevance_advanced.ipynb
│││├search+relevance.html
│││├search+relevance_advanced.html
│││├<.ipynb_checkpoints>
││││├search relevance_advanced-checkpoint.ipynb
││││└search relevance-checkpoint.ipynb
│├<lecture05>
││├energy_forecasting_notebooks.zip
││└subway_prediction_notebook.zip
│├<lecture06>
││├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
││├<img>
│││├chi_square.png
│││└RGBHistogram.jpg
││├<猫狗的数据>
│││├cats-vs-dogs.txt
│││├sample_submission.csv
│││├test.zip
│││└train.zip
│├<lecture07>
││├data.zip
││├Kaggle event recommendation competition.ipynb
││├kaggle-event-recommendation-rank1.zip
││└Rossmann_Store_Sales_competition.ipynb
│├<lecture08>
││└PPD_RiskControl_Competition.zip
├<课件>
│├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
│├<lecture01>
││├Kaggle第01课：机器学习算法、工具与流程概述.pdf
││└分享的链接.txt
│├<lecture02>
││└Kaggle第02课：经济金融相关问题.pdf
│├<lecture03>
││├kaggle-2014-criteo.pdf
││├kaggle-avazu.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
│├<lecture04>
││└Kaggle第四课.pdf
│├<lecture05>
││└第5课：能源预测与分配问题.pdf
│├<lecture07>
││└第7课：推荐与销量预测相关问题.pdf
│├<lecture08>
││├第8课：金融风控问题.pdf
││└金融风控大赛解决方案.pdf
**** Hidden Message *****
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谢谢分享，和三通IT一起成长 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 学技术来三通IT技术学院！ 深度学习最前沿技术 Kaggle案例实战课程