Skip to content

Latest commit

 

History

History
83 lines (80 loc) · 19.1 KB

README.md

File metadata and controls

83 lines (80 loc) · 19.1 KB
Raw

MEDIUM_NoteBook

Repository containing notebooks of my posts on MEDIUM.

To be notified every time a new post is published, SUBSCRIBE HERE.

"Buy Me A Coffee"

Posts ordered by most recent publishing date

  • Hitting Time Forecasting: The Other Way for Time Series Probabilistic Forecasting [post]|[code]
  • Forecasting with Granger Causality: Checking for Time Series Spurious Correlations [post]|[code]
  • Hacking Causal Inference: Synthetic Control with ML approaches [post]|[code]
  • Model Selection with Imbalance Data: Only AUC may Not Save you [post]|[code]
  • PCA for Multivariate Time Series: Forecasting Dynamic High-Dimensional Data [post]|[code]
  • Hacking Statistical Significance: Hypothesis Testing with ML Approaches [post]|[code]
  • Time Series Forecasting with Conformal Prediction Intervals: Scikit-Learn is All you Need [post]|[code]
  • Rethinking Survival Analysis: How to Make your Model Produce Survival Curves [post]|[code]
  • Extreme Churn Prediction: Forecasting Without Features [post]|[code]
  • Forecast Time Series with Missing Values: Beyond Linear Interpolation [post]|[code]
  • Forecasting Uncertainty with Linear Models like in Deep Learning [post]|[code]
  • Time Series Forecasting with Feature Selection: Why you may need it [post]|[code]
  • Anomaly Detection in Multivariate Time Series with Network Graphs [post]|[code]
  • How to Improve Recursive Time Series Forecasting [post]|[code]
  • Retrain, or not Retrain? Online Machine Learning with Gradient Boosting [post]|[code]
  • Data Drift Explainability: Interpretable Shift Detection with NannyML [post]|[code]
  • Word2Vec with Time Series: A Transfer Learning Approach [post]|[code]
  • SHAP for Drift Detection: Effective Data Shift Monitoring [post]|[code]
  • Forecasting with Trees: Hybrid Classifiers for Time Series [post]|[code]
  • Boruta SHAP for Temporal Feature Selection [post]|[code]
  • Forecasting with Trees: Hybrid Modeling for Time Series [post]|[code]
  • Recursive Feature Selection: Addition or Elimination? [post]|[code]
  • Improve Random Forest with Linear Models [post]|[code]
  • Is Gradient Boosting good as Prophet for Time Series Forecasting? [post]|[code]
  • Linear Boosting with Automated Features Engineering [post]|[code]
  • Improve Linear Regression for Time Series Forecasting [post]|[code]
  • Boruta and SHAP for better Feature Selection [post]|[code]
  • Explainable AI with Linear Trees [post]|[code]
  • SHAP for Feature Selection and HyperParameter Tuning [post]|[code]
  • Model Tree: handle Data Shifts mixing Linear Model and Decision Tree [post]|[code]
  • Add Prediction Intervals to your Forecasting Model [post]|[code]
  • Linear Tree: the perfect mix of Linear Model and Decision Tree [post]
  • ARIMA for Classification with Soft Labels [post]|[code]
  • Advanced Permutation Importance to Explain Predictions [post]|[code]
  • Time Series Bootstrap in the age of Deep Learning [post]|[code]
  • Anomaly Detection with Extreme Value Analysis [post]|[code]
  • Time Series generation with VAE LSTM [post]|[code]
  • Extreme Event Time Series Preprocessing [post]|[code]
  • One-Class Neural Network in Keras [post]|[code]
  • Real-Time Time Series Anomaly Detection [post]|[code]
  • Entropy Application in the Stock Market [post]|[code]
  • Time Series Smoothing for better Forecasting [post]|[code]
  • Time Series Smoothing for better Clustering [post]|[code]
  • Predictive Maintenance with ResNet [post]|[code]
  • Neural Networks Ensemble [post]|[code]
  • Anomaly Detection in Multivariate Time Series with VAR [post]|[code]
  • Corr2Vec: a WaveNet architecture for Feature Engineering in Financial Market [post]|[code]
  • Siamese and Dual BERT for Multi Text Classification [post]|[code]
  • Time Series Forecasting with Graph Convolutional Neural Network [post]|[code]
  • Neural Network Calibration with Keras [post]|[code]
  • Combine LSTM and VAR for Multivariate Time Series Forecasting [post]|[code]
  • Feature Importance with Time Series and Recurrent Neural Network [post]|[code]
  • Group2Vec for Advance Categorical Encoding [post]|[code]
  • Survival Analysis with Deep Learning in Keras [post]|[code]
  • Survival Analysis with LightGBM plus Poisson Regression [post]|[code]
  • Predictive Maintenance: detect Faults from Sensors with CRNN and Spectrograms [post]|[code]
  • Multi-Sample Dropout in Keras [post]|[code]
  • When your Neural Net doesn’t know: a bayesian approach with Keras [post]|[code]
  • Dynamic Meta Embeddings in Keras [post]|[code]
  • Predictive Maintenance with LSTM Siamese Network [post]|[code]
  • Text Data Augmentation makes your model stronger [post]|[code]
  • Anomaly Detection with Permutation Undersampling and Time Dependency [post]|[code]
  • Time2Vec for Time Series features encoding [post]|[code]
  • Automate Data Cleaning with Unsupervised Learning [post]|[code]
  • People Tracking with Machine Learning [post]|[code]
  • Time Series Clustering and Dimensionality Reduction [post]|[code]
  • Anomaly Detection in Images [post]|[code]
  • Feature Importance with Neural Network [post]|[code]
  • Anomaly Detection with LSTM in Keras [post]|[code]
  • Dress Segmentation with Autoencoder in Keras [post]|[code]
  • Extreme Event Forecasting with LSTM Autoencoders [post]|[code]
  • Zalando Dress Recommendation and Tagging [post]|[code]
  • Remaining Life Estimation with Keras [post]|[code]
  • Quality Control with Machine Learning [post]|[code]
  • Predictive Maintenance: detect Faults from Sensors with CNN [post]|[code]