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Name		Name	Last commit message	Last commit date
pic		pic
02.1_neuralprophet_bike_share_trend_seasonality.ipynb		02.1_neuralprophet_bike_share_trend_seasonality.ipynb
02.2_neuralprophet_bike_share_AR_lagged.ipynb		02.2_neuralprophet_bike_share_AR_lagged.ipynb
02.HBOS.ipynb		02.HBOS.ipynb
03. ECOD.ipynb		03. ECOD.ipynb
04.iForest.ipynb		04.iForest.ipynb
05. PCA.ipynb		05. PCA.ipynb
05_1_ARIMA_avocado.ipynb		05_1_ARIMA_avocado.ipynb
06. OCSVM.ipynb		06. OCSVM.ipynb
06.1_tree_based_time_series_forecasting.ipynb		06.1_tree_based_time_series_forecasting.ipynb
06.2_multi_step_forecasts.ipynb		06.2_multi_step_forecasts.ipynb
07. GMM.ipynb		07. GMM.ipynb
08. KNN.ipynb		08. KNN.ipynb
09. LOF.ipynb		09. LOF.ipynb
10. CBLOF.ipynb		10. CBLOF.ipynb
10.0_Linear Regression for Multi-period Time Series Forecasts with Uncertainty.ipynb		10.0_Linear Regression for Multi-period Time Series Forecasts with Uncertainty.ipynb
11. XGBOD.ipynb		11. XGBOD.ipynb
12. Autoencoders.ipynb		12. Autoencoders.ipynb
12.0_deepAR_gluonTS.ipynb		12.0_deepAR_gluonTS.ipynb
12_1_gluonTS_stocks.ipynb		12_1_gluonTS_stocks.ipynb
14.0_Tree-based Time Series Probabilistic forecasting.ipynb		14.0_Tree-based Time Series Probabilistic forecasting.ipynb
16.0_TFT_Ecuador_sales..ipynb		16.0_TFT_Ecuador_sales..ipynb
17_0_lag_llama_walmart.ipynb		17_0_lag_llama_walmart.ipynb
20220804_Transfer_learning_for_Image_Classification.ipynb		20220804_Transfer_learning_for_Image_Classification.ipynb
A data scientist toolkit.ipynb		A data scientist toolkit.ipynb
A wide variety of models for multi-class classification.ipynb		A wide variety of models for multi-class classification.ipynb
Algo_trading_01_TA.ipynb		Algo_trading_01_TA.ipynb
Algo_trading_02_backtesting.ipynb		Algo_trading_02_backtesting.ipynb
AntPlot.py		AntPlot.py
Change point detection.ipynb		Change point detection.ipynb
DL5-variational-autoencoder.ipynb		DL5-variational-autoencoder.ipynb
Deploy your machine learning model using streamlit.ipynb		Deploy your machine learning model using streamlit.ipynb
Dickens_A_Tale_of_Two_Cities.txt		Dickens_A_Tale_of_Two_Cities.txt
Explain Your Model with Microsoft's InterpretML-Github.ipynb		Explain Your Model with Microsoft's InterpretML-Github.ipynb
Explain any models with the SHAP values - the KernelExplainer for article.ipynb		Explain any models with the SHAP values - the KernelExplainer for article.ipynb
Explain your model with LIME for article.ipynb		Explain your model with LIME for article.ipynb
Explain your model with the SHAP values for article.ipynb		Explain your model with the SHAP values for article.ipynb
From Quantile Regression to Quantile Random Forests.ipynb		From Quantile Regression to Quantile Random Forests.ipynb
From logistic to deep learning.ipynb		From logistic to deep learning.ipynb
From regression to RNN.ipynb		From regression to RNN.ipynb
HNSW.ipynb		HNSW.ipynb
Histogram-based Outlier Score.ipynb		Histogram-based Outlier Score.ipynb
Kalman Filter.ipynb		Kalman Filter.ipynb
Keras MNIST.ipynb		Keras MNIST.ipynb
LSH.ipynb		LSH.ipynb
Luminaire.ipynb		Luminaire.ipynb
NotCoolYet.py		NotCoolYet.py
Plot with Plotly for article.ipynb		Plot with Plotly for article.ipynb
PyOD Tutorial - autoencoder.ipynb		PyOD Tutorial - autoencoder.ipynb
RLHF_for_algorithmic_trading.ipynb		RLHF_for_algorithmic_trading.ipynb
ROUGE.ipynb		ROUGE.ipynb
Revisiting the CM the ROC and the PR.ipynb		Revisiting the CM the ROC and the PR.ipynb
S3_sample.ipynb		S3_sample.ipynb
TA in R for article.md		TA in R for article.md
Taylor_rule.py		Taylor_rule.py
The SHAP Values with H2o Models.ipynb		The SHAP Values with H2o Models.ipynb
The SHAP values with More Charts for article.ipynb		The SHAP values with More Charts for article.ipynb
The Taylor rule.ipynb		The Taylor rule.ipynb
The_VGG_16_With_Tensorflow_in_Google_Colab.ipynb		The_VGG_16_With_Tensorflow_in_Google_Colab.ipynb
The_VGG_16_with_Google_Co_lab.ipynb		The_VGG_16_with_Google_Co_lab.ipynb
Time Series Anomaly Detection with Prophet for article.ipynb		Time Series Anomaly Detection with Prophet for article.ipynb
Undersampling and oversampling.ipynb		Undersampling and oversampling.ipynb
Use seaborn to do beautiful plots easy-git.ipynb		Use seaborn to do beautiful plots easy-git.ipynb
VGG-16.ipynb		VGG-16.ipynb
VeryCool.py		VeryCool.py
Waterfall plot for the SHAP of any models class.ipynb		Waterfall plot for the SHAP of any models class.ipynb
cookie_OOP.ipynb		cookie_OOP.ipynb
deploy_model.py		deploy_model.py
imagenet_classes.txt		imagenet_classes.txt
my_function.py		my_function.py
sample.mp3		sample.mp3
stock.py		stock.py
stock2.py		stock2.py
streamlit_model_performance.py		streamlit_model_performance.py
time_series_08_monte_carlo_simulation.ipynb		time_series_08_monte_carlo_simulation.ipynb
use_NotCoolYet.py		use_NotCoolYet.py
use_VeryCool.py		use_VeryCool.py
voice-change-Ivy.mp3		voice-change-Ivy.mp3
voice-change-Joanna.mp3		voice-change-Joanna.mp3
wavenet_github.py		wavenet_github.py
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