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Blogroll | Top
List of posts by date of publication | Top
- Quasi-randomized nnetworks in Julia, Python and R
Nov 27, 2023
- A plethora of datasets at your fingertips
Nov 20, 2023
- A classifier that's very accurate (and deep)
Nov 12, 2023
- mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
Nov 5, 2023
- AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
Oct 29, 2023
- AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
Oct 22, 2023
- Version v0.14.0 of nnetsauce for R and Python
Oct 16, 2023
- A diffusion model: G2++
Oct 9, 2023
- Diffusion models in ESGtoolkit + announcements
Oct 2, 2023
- An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
Sep 25, 2023
- (News from) forecasting in Python with ahead (progress bars and plots)
Sep 18, 2023
- Forecasting in Python with ahead
Sep 11, 2023
- Risk-neutralize simulations
Sep 4, 2023
- Comparing cross-validation results using crossval_ml and boxplots
Aug 27, 2023
- Reminder
Apr 30, 2023
- Did you ask ChatGPT about who you are?
Apr 16, 2023
- A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
Apr 2, 2023
- Simple interfaces to the forecasting API
Nov 23, 2022
- A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
Nov 2, 2022
- Prediction intervals (not only) for Boosted Configuration Networks in Python
Oct 5, 2022
- Boosted Configuration (neural) Networks Pt. 2
Sep 3, 2022
- Boosted Configuration (_neural_) Networks for classification
Jul 21, 2022
- 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
Categories | Top
Forecasting
- Version v0.14.0 of nnetsauce for R and Python
- An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
- (News from) forecasting in Python with ahead (progress bars and plots)
- Forecasting in Python with ahead
- Simple interfaces to the forecasting API
- A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
- 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
Misc
- A plethora of datasets at your fingertips
- Risk-neutralize simulations
- Comparing cross-validation results using crossval_ml and boxplots
- Reminder
- Did you ask ChatGPT about who you are?
- A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
- Boosted Configuration (_neural_) Networks for classification
- 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
- Quasi-randomized nnetworks in Julia, Python and R
- A plethora of datasets at your fingertips
- A classifier that's very accurate (and deep)
- mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
- AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
- AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
- Version v0.14.0 of nnetsauce for R and Python
- An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
- (News from) forecasting in Python with ahead (progress bars and plots)
- Forecasting in Python with ahead
- Did you ask ChatGPT about who you are?
- A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
- Simple interfaces to the forecasting API
- A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
- Prediction intervals (not only) for Boosted Configuration Networks in 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
R
- Quasi-randomized nnetworks in Julia, Python and R
- A plethora of datasets at your fingertips
- A classifier that's very accurate (and deep)
- mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
- Version v0.14.0 of nnetsauce for R and Python
- A diffusion model: G2++
- Diffusion models in ESGtoolkit + announcements
- Risk-neutralize simulations
- Comparing cross-validation results using crossval_ml and boxplots
- Did you ask ChatGPT about who you are?
- A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
- Simple interfaces to the forecasting API
- A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
- Boosted Configuration (neural) Networks Pt. 2
- Boosted Configuration (_neural_) Networks for classification
- 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