- A Machine Learning workflow using Techtonique Jun 6, 2022
- Super Mario Bros © in the browser using PyScript May 8, 2022
- News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
- Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
- New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
- A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
- Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
- Forecasting with `ahead` (Python version) Dec 13, 2021
- Tuning and interpreting LSBoost Nov 15, 2021
- Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
- Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
- Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
- Forecasting with `ahead` Oct 15, 2021
- Classification using linear regression Sep 26, 2021
- `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
- parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
- `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
- Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
- Hyperparameters tuning with GPopt Jun 11, 2021
- A forecasting tool (API) with examples in curl, R, Python May 28, 2021
- Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
- Bayesian Optimization with GPopt Apr 16, 2021
- Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
- Explaining xgboost predictions with the teller Mar 12, 2021
- An infinity of time series models in nnetsauce Mar 6, 2021
- New activation functions in mlsauce's LSBoost Feb 12, 2021
- 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
- A deeper learning architecture in nnetsauce Dec 18, 2020
- Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
- Bayesian forecasting for uni/multivariate time series Dec 4, 2020
- Generalized nonlinear models in nnetsauce Nov 28, 2020
- Boosting nonlinear penalized least squares Nov 21, 2020
- Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
- NEWS Oct 30, 2020
- A glimpse into my PhD journey Oct 23, 2020
- Submitting R package to CRAN Oct 16, 2020
- Simulation of dependent variables in ESGtoolkit Oct 9, 2020
- Forecasting lung disease progression Oct 2, 2020
- New nnetsauce Sep 25, 2020
- Technical documentation Sep 18, 2020
- A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
- Back next week, and a few announcements Sep 4, 2020
- Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
- LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
- nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
- Maximizing your tip as a waiter (Part 2) Jul 10, 2020
- New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
- Announcements Jun 26, 2020
- Parallel AdaOpt classification Jun 19, 2020
- Comments section and other news Jun 12, 2020
- Maximizing your tip as a waiter Jun 5, 2020
- AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
- AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
- AdaOpt May 15, 2020
- Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
- Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
- Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
- Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
- Grid search cross-validation using crossval Apr 10, 2020
- Documentation for the querier, a query language for Data Frames Apr 3, 2020
- Time series cross-validation using crossval Mar 27, 2020
- On model specification, identification, degrees of freedom and regularization Mar 20, 2020
- Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
- R notebooks for nnetsauce Mar 6, 2020
- Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
- Create a specific feed in your Jekyll blog Feb 21, 2020
- Git/Github for contributing to package development Feb 14, 2020
- Feedback forms for contributing Feb 7, 2020
- nnetsauce for R Jan 31, 2020
- A new version of nnetsauce (v0.3.1) Jan 24, 2020
- ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
- Search bar, new year 2020 Jan 10, 2020
- 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
- Understanding model interactions with the `teller` Dec 13, 2019
- Using the `teller` on a classifier Dec 6, 2019
- Benchmarking the querier's verbs Nov 29, 2019
- Composing the querier's verbs for data wrangling Nov 22, 2019
- Comparing and explaining model predictions with the teller Nov 15, 2019
- Tests for the significance of marginal effects in the teller Nov 8, 2019
- Introducing the teller Nov 1, 2019
- Introducing the querier Oct 25, 2019
- Prediction intervals for nnetsauce models Oct 18, 2019
- Using R in Python for statistical learning/data science Oct 11, 2019
- Model calibration with `crossval` Oct 4, 2019
- Bagging in the nnetsauce Sep 25, 2019
- Adaboost learning with nnetsauce Sep 18, 2019
- Change in blog's presentation Sep 4, 2019
- nnetsauce on Pypi Jun 5, 2019
- More nnetsauce (examples of use) May 9, 2019
- nnetsauce Mar 13, 2019
- crossval Mar 13, 2019
- test Mar 10, 2019
I will publish some human-readable content here. Want to get notified (no spam, guaranteed)? Then use the subscription box below, or subscribe via RSS. A list of previous posts can be found here, and a list of categories here. You can also use the search bar -- appearing before this paragraph -- if you're looking for a more specific keyword. Having any other inquiry related to the content published here? Easiest/safest way is to email me at: thierry dot moudiki at pm dot me.
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DataBases
- Technical documentation
- Maximizing your tip as a waiter
- Documentation for the querier, a query language for Data Frames
- Import data into the querier (now on Pypi), a query language for Data Frames
- Feedback forms for contributing
- 2019 Recap, the nnetsauce, the teller and the querier
- Benchmarking the querier's verbs
- Composing the querier's verbs for data wrangling
- Introducing the querier
ExplainableML
- A Machine Learning workflow using Techtonique
- Explaining a Keras _neural_ network predictions with the-teller
- Explaining xgboost predictions with the teller
- New activation functions in mlsauce's LSBoost
- Generalized nonlinear models in nnetsauce
- Boosting nonlinear penalized least squares
- Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
- Technical documentation
- Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
- LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
- Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
- Feedback forms for contributing
- 2019 Recap, the nnetsauce, the teller and the querier
- Understanding model interactions with the `teller`
- Using the `teller` on a classifier
- Comparing and explaining model predictions with the teller
- Tests for the significance of marginal effects in the teller
- Introducing the teller
LSBoost
- A Machine Learning workflow using Techtonique
- Compatibility of nnetsauce and mlsauce with scikit-learn
- New activation functions in mlsauce's LSBoost
- Boosting nonlinear penalized least squares
- Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
- LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
Misc
- Super Mario Bros © in the browser using PyScript
- News from ESGtoolkit, ycinterextra, and nnetsauce
- Time series cross-validation using `crossvalidation` (Part 2)
- Fast and scalable forecasting with ahead::ridge2f
- Automatic Forecasting with `ahead::dynrmf` and Ridge regression
- Forecasting with `ahead`
- Documentation and source code for GPopt, a package for Bayesian optimization
- Hyperparameters tuning with GPopt
- A forecasting tool (API) with examples in curl, R, Python
- Bayesian Optimization with GPopt Part 2 (save and resume)
- Bayesian Optimization with GPopt
- 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
- Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
- NEWS
- A glimpse into my PhD journey
- Forecasting lung disease progression
- New nnetsauce
- Technical documentation
- A new version of nnetsauce, and a new Techtonique website
- Back next week, and a few announcements
- Maximizing your tip as a waiter (Part 2)
- New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees
- Announcements
- Comments section and other news
- Maximizing your tip as a waiter
- Custom errors for cross-validation using crossval::crossval_ml
- Encoding your categorical variables based on the response variable and correlations
- Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
- Grid search cross-validation using crossval
- Time series cross-validation using crossval
- On model specification, identification, degrees of freedom and regularization
- Create a specific feed in your Jekyll blog
- Git/Github for contributing to package development
- Feedback forms for contributing
- Change in blog's presentation
- test
Python
- A Machine Learning workflow using Techtonique
- Super Mario Bros © in the browser using PyScript
- News from ESGtoolkit, ycinterextra, and nnetsauce
- Explaining a Keras _neural_ network predictions with the-teller
- New version of nnetsauce -- various quasi-randomized networks
- A dashboard illustrating bivariate time series forecasting with `ahead`
- Forecasting with `ahead` (Python version)
- Tuning and interpreting LSBoost
- Classification using linear regression
- Documentation and source code for GPopt, a package for Bayesian optimization
- Hyperparameters tuning with GPopt
- A forecasting tool (API) with examples in curl, R, Python
- Bayesian Optimization with GPopt Part 2 (save and resume)
- Bayesian Optimization with GPopt
- Compatibility of nnetsauce and mlsauce with scikit-learn
- Explaining xgboost predictions with the teller
- An infinity of time series models in nnetsauce
- 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
- A deeper learning architecture in nnetsauce
- Generalized nonlinear models in nnetsauce
- Boosting nonlinear penalized least squares
- Technical documentation
- A new version of nnetsauce, and a new Techtonique website
- Back next week, and a few announcements
- Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
- LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
- nnetsauce version 0.5.0, randomized neural networks on GPU
- Maximizing your tip as a waiter (Part 2)
- Parallel AdaOpt classification
- Maximizing your tip as a waiter
- AdaOpt classification on MNIST handwritten digits (without preprocessing)
- AdaOpt
- Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
- Encoding your categorical variables based on the response variable and correlations
- Documentation for the querier, a query language for Data Frames
- Import data into the querier (now on Pypi), a query language for Data Frames
- Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
- A new version of nnetsauce (v0.3.1)
- Using R in Python for statistical learning/data science
- nnetsauce on Pypi
QuasiRandomizedNN
- New version of nnetsauce -- various quasi-randomized networks
- Tuning and interpreting LSBoost
- Classification using linear regression
- An infinity of time series models in nnetsauce
- A deeper learning architecture in nnetsauce
- Classify penguins with nnetsauce's MultitaskClassifier
- Bayesian forecasting for uni/multivariate time series
- New nnetsauce
- Technical documentation
- A new version of nnetsauce, and a new Techtonique website
- Back next week, and a few announcements
- nnetsauce version 0.5.0, randomized neural networks on GPU
- R notebooks for nnetsauce
- Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
- Feedback forms for contributing
- nnetsauce for R
- A new version of nnetsauce (v0.3.1)
- 2019 Recap, the nnetsauce, the teller and the querier
- Prediction intervals for nnetsauce models
- Bagging in the nnetsauce
- Adaboost learning with nnetsauce
- nnetsauce on Pypi
- More nnetsauce (examples of use)
- nnetsauce
R
- News from ESGtoolkit, ycinterextra, and nnetsauce
- New version of nnetsauce -- various quasi-randomized networks
- A dashboard illustrating bivariate time series forecasting with `ahead`
- Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
- Time series cross-validation using `crossvalidation` (Part 2)
- Fast and scalable forecasting with ahead::ridge2f
- Automatic Forecasting with `ahead::dynrmf` and Ridge regression
- Forecasting with `ahead`
- `crossvalidation` and random search for calibrating support vector machines
- parallel grid search cross-validation using `crossvalidation`
- `crossvalidation` on R-universe, plus a classification example
- A forecasting tool (API) with examples in curl, R, Python
- An infinity of time series models in nnetsauce
- New activation functions in mlsauce's LSBoost
- 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
- Classify penguins with nnetsauce's MultitaskClassifier
- Bayesian forecasting for uni/multivariate time series
- Boosting nonlinear penalized least squares
- Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
- Submitting R package to CRAN
- Simulation of dependent variables in ESGtoolkit
- Forecasting lung disease progression
- Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
- LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
- nnetsauce version 0.5.0, randomized neural networks on GPU
- Maximizing your tip as a waiter (Part 2)
- Maximizing your tip as a waiter
- AdaOpt classification on MNIST handwritten digits (without preprocessing)
- AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
- Custom errors for cross-validation using crossval::crossval_ml
- Encoding your categorical variables based on the response variable and correlations
- Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
- Grid search cross-validation using crossval
- Time series cross-validation using crossval
- On model specification, identification, degrees of freedom and regularization
- R notebooks for nnetsauce
- Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
- Feedback forms for contributing
- nnetsauce for R
- ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
- Using R in Python for statistical learning/data science
- Model calibration with `crossval`
- crossval
mlsauce
- A Machine Learning workflow using Techtonique
- Tuning and interpreting LSBoost
- New activation functions in mlsauce's LSBoost
- Technical documentation
- Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
- LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
- Maximizing your tip as a waiter (Part 2)
- New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees
- Parallel AdaOpt classification
- Maximizing your tip as a waiter
- AdaOpt classification on MNIST handwritten digits (without preprocessing)
- AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
- AdaOpt