DataScience
workshop for data science
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Download Latest Version (.zip)- cifar.lst
- cifar_label.xlsx
- 3D convolutional neural networks for human action recognition (2013), S. Ji et al. [pdf] .pdf
- Batch Normalization Accelerating Deep Network Training.pdf
- Reinforcement Learning An Introduction.pdf
- ResNet(Deep Residual Learning for Image Recognition).pdf
- SESSION-BASED RECOMMENDATIONS WITH RNN.pdf
- XGBoost-A Scalable Tree Boosting System.pdf
- A Few Useful Things to Know about Machine Learning.pdf
- Blending.py
- Blending_pro.py
- blending_pro2.py
- example_twoClass_higgs.R
- fun_h2o_ensemble.R
- h2o_stacking_example.R
- kaggle_rankavg.py
- ottoHomeBagG4.R
- Randombitsregression_a strong general predictor for big data.pdf
- imageresitration_theRway.html
- imageresitration_theRway.Rmd
- ScoresExample.png
- ScoresExample_small.png
- AssemblyInfo.cs
- Settings.Designer.cs
- Settings.settings
- App.config
- FeatureInteraction.cs
- FeatureInteractions.cs
- FeatureScoreComparer.cs
- FIScoreComparer.cs
- GlobalSettings.cs
- GlobalStats.cs
- packages.config
- Program.cs
- SplitValueHistogram.cs
- XgbFeatureInteractions.csproj
- XgbModel.cs
- XgbModelParser.cs
- XgbTree.cs
- XgbTreeNode.cs
- .gitattributes
- .gitignore
- README.md
- XgbFeatureInteractions.sln
- data_augmentation.py
- pred_theano_RNN.py
- Rtsne_visiual.R
- train_keras_model.py
- train_theano_RNN.py
- xgboostVSgbm.R
- calculate_NDCG.py
- plot_ndcgs.py
- relevances.py
- validate.py
- data.ipynb
- data_iter.py
- initializer.ipynb
- mixed.ipynb
- module.ipynb
- ndarray.ipynb
- optimizer.ipynb
- symbol.ipynb
- mnist.ipynb
- label
- finetune-local.ipynb
- finetune.ipynb
- predict.ipynb
- cifar-100.ipynb
- cifar10-recipe.ipynb
- class_active_maps.ipynb
- cnn-text-classification.ipynb
- composite_symbol.ipynb
- predict-with-pretrained-model.ipynb
- README.md
- simple_bind.ipynb
- tutorial.ipynb
- autoencoder.py
- BCD_one.py
- cal_precision.py
- cal_rec.py
- cdl.py
- collaborative-dl.ipynb
- data.py
- evaluate_CDL.py
- mf.py
- mnist_data.py
- mnist_sae.py
- model.py
- mult.py
- README.md
- show_recommendation.py
- solver.py
- test_BCD.py
- tryinv.py
- lstm.ipynb
- bucket_io.py
- char_lstm.ipynb
- lstm.py
- lstm_bucketing.py
- matrix_factorization.ipynb
- mnist.ipynb
- mnist_demo.py
- predict_imagenet.ipynb
- rnn.py
- rnn_model.py
- outline.ipynb
- quora_train_model.py
- quora_tsne.py
- splitcheckindate.py
- splitcheckindate.sql
- HighPerformancePython.ipynb
- README.md
- Viterbi.py
- Inception_BN-symbol.json
- mean_224.nd
- synset.txt
- .Rhistory
- crime.dta
- Untitled.R
- Baker_Lake.jpg
- google็correlation between box index&search index.jpg
- igraph_networksample.R
- mxnet_class®ression.R
- mxnet_handwrittenClass.R
- Rtsne.R
- Rweibo.R
- seeclickfix.R
- the-grammar.r
- word_cloud.R
- global.R
- server.R
- ui.R
- chron_2.3-47.tgz
- data.table_1.9.4.tgz
- ggplot2_0.9.3.1.tar.gz
- R-3.2.2.tar.gz
- README.md
- tau_0.0-18.tgz
- xgboost_0.4-2.tgz
- Making Faceted Heatmaps with ggplot2.R
- 10-mengshengwang-actuarial-science.pptx
- 11-chenyibo-social-network[31593].pdf
- 12-duanminming-ORE.ppt
- 13-huangjinshan-Rcpp[31697].pdf
- 15-luyinbo-HMM[8148].pdf
- 16-haozhiheng-DOE[31046].pdf
- 17-lixinhai-random-forest[25562].pdf
- 2-zhoutao-recommendation[4365].pdf
- 4-qiuyixuan-bigdata[5681].pdf
- 7-weitaiyun-knitr[32632].pdf
- 9-dengyishuo-finance[3840].pdf
- lijian-BI.pptx
- changyou-Julia-20130518[28601].pdf
- ChinaR-2013-Yihui-Xie.html
- Data Analysis Tools in an Age of.PDF
- data mining with R_beijing[5119].PDF
- DATA-MINING้ฒ็ซฏๆฑบ็ญๅนณๅฐCDMS-Smart-Score-II-ไปฅ-R-็บๅบ็ค-REVISED.pptx
- DISPLAYHTS_A R PACKAGE FOR DISPLAYING DATA AND RESULTS FROM HIGH-THROUGHPUT SCREENING EXPERIMENTS .Zhang_.Xiaohua.pptx
- Mstoolkit,ml5vis็ไป็ปRweibo,hlijian_ChinaR20130518.ppt
- On the ultrahigh dimensional linear.PDF
- quality-evaluation-and-ordering-of-user-generated-content-wanghao[13487].pdf
- R-Case-Study-from-EBAY-DDI.pptx
- Rๅทฅ็จๅฎ็ฐไผ่ฎฎ-้ฟ็จณ[20128].PDF
- Web-Scraping-with-R-XiaoNan[28845].pdf
- ็จRๅWinBUGSๅฎ็ฐ่ดๅถๆฏๅ็บงๆจกๅ Bayesian hierarchical modeling using R and WinBUGS.PDF
- ็ฝ็ป่ๆ ็ๆต_็่ดบ_201305.rar
- Gaming-Webinar[17826].pdf
- README.md
- RServe็ๆญๅปบ.md
- symbol_alexnet.R
- symbol_resnet.R
- symbol_vgg.R
- im2CNN.ipynb
- im2rec.ipynb
- im2rec.py
- ImageRegistration.Rmd
- nn_lr-0010.params
- nn_lr-symbol.json
- .Rhistory
- Deep learning in R using MXNet.pdf
- feature_extract.ipynb
- load_img_process.R
- pred_with_pretrain_model.R
- README.md
- train_cnn_model.R
- train_mnist.R
- train_model.R
- train_nnlr_sample.R
- .gitignore
- Keras_Cheat_Sheet_Python.pdf
- microsoft-machine-learning-algorithm-cheat-sheet-v7.pdf
- PandasPythonForDataScience.pdf
- PythonForDataScience.pdf
- README.md
- Scikit_Learn_Cheat_Sheet_Python.pdf
# Installation Guide
1. Get the code
git clone https://github.com/pjpan/DataScience
Downloads the entire project code from GitHub to your computer.
cd DataScience
Moves into the project folder you just downloaded.
2. .NET
Medium RecommendedPrerequisites
โ ๏ธ This is a large repository, so this method may point to an internal sub-package rather than the actual core product. Check the full README as well.
cd MLtech/xgbfi
This project's files live in a subfolder, so move into it first.
dotnet restore
Downloads the packages the project depends on.
dotnet run
Builds the project and runs it immediately.
After dotnet run, check the message or address shown in the terminal.
// repository documentation
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