logo

Blog


You can expect to read a post here every Sunday or Monday, mostly about Techtonique. Want to get notified? Subscribe via RSS.

A list of previous posts can be found below, and a list of categories here. Use the search bar appearing before this paragraph if you're looking for a more specific keyword.

You can also me on GitHub or Hire me on LinkedIn or Hire me on Malt or Hire me on Fiverr or Hire me on Upwork

Having any other inquiry related to the content published here? Easiest/fastest/safest way is to send an email to: thierry dot moudiki at gmail dot com.



Blogroll | Top

List of posts by date of publication | Top

  1. GLMNet in Python: Generalized Linear Models Nov 18, 2024
  2. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting Nov 10, 2024
  3. Predictive scenarios simulation in R, Python and Excel using Techtonique API Nov 3, 2024
  4. Chat with your tabular data in www.techtonique.net Oct 30, 2024
  5. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting Oct 28, 2024
  6. R editor and SQL console (in addition to Python editors) in www.techtonique.net Oct 21, 2024
  7. R and Python consoles + JupyterLite in www.techtonique.net Oct 15, 2024
  8. Gradient-Boosting anything (alert: high performance): Part2, R version Oct 14, 2024
  9. Gradient-Boosting anything (alert: high performance) Oct 6, 2024
  10. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge Oct 4, 2024
  11. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution Oct 2, 2024
  12. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood Sep 30, 2024
  13. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization Sep 25, 2024
  14. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead Sep 16, 2024
  15. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine Sep 10, 2024
  16. New versions of nnetsauce and ahead Sep 9, 2024
  17. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Sep 2, 2024
  18. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2 Aug 30, 2024
  19. R package development workflow (assuming you're using macOS or Linux) Aug 27, 2024
  20. A new method for deriving a nonparametric confidence interval for the mean Aug 26, 2024
  21. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R Aug 19, 2024
  22. Bayesian (nonlinear) adaptive learning Aug 12, 2024
  23. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting Aug 5, 2024
  24. Copulas for uncertainty quantification in time series forecasting Jul 28, 2024
  25. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app) Jul 22, 2024
  26. learningmachine for Python (new version) Jul 15, 2024
  27. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification Jul 8, 2024
  28. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news) Jul 3, 2024
  29. 10 uncertainty quantification methods in nnetsauce forecasting Jul 1, 2024
  30. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Jun 24, 2024
  31. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Jun 17, 2024
  32. Automated hyperparameter tuning using any conformalized surrogate Jun 9, 2024
  33. Recognizing handwritten digits with Ridge2Classifier Jun 3, 2024
  34. Forecasting the Economy May 27, 2024
  35. A detailed introduction to Deep Quasi-Randomized 'neural' networks May 19, 2024
  36. Probability of receiving a loan; using learningmachine May 12, 2024
  37. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction May 6, 2024
  38. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables Apr 29, 2024
  39. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering Apr 21, 2024
  40. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor Apr 15, 2024
  41. Conformalized predictive simulations for univariate time series on more than 250 data sets Apr 7, 2024
  42. learningmachine v1.1.2: for Python Apr 1, 2024
  43. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant' Mar 25, 2024
  44. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1 Mar 18, 2024
  45. Multiple examples of Machine Learning forecasting with ahead Mar 11, 2024
  46. rtopy (v0.1.1): calling R functions in Python Mar 4, 2024
  47. ahead forecasting (v0.10.0): fast time series model calibration and Python plots Feb 26, 2024
  48. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other? Feb 19, 2024
  49. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0) Feb 11, 2024
  50. Tuning Machine Learning models with GPopt's new version Part 2 Feb 5, 2024
  51. Tuning Machine Learning models with GPopt's new version Jan 29, 2024
  52. Subsampling continuous and discrete response variables Jan 22, 2024
  53. DeepMTS, a Deep Learning Model for Multivariate Time Series Jan 15, 2024
  54. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce Jan 8, 2024
  55. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest Jan 1, 2024
  56. A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction Dec 25, 2023
  57. Diffusion models in Python with esgtoolkit (Part2) Dec 18, 2023
  58. Diffusion models in Python with esgtoolkit Dec 11, 2023
  59. Julia packaging at the command line Dec 4, 2023
  60. Quasi-randomized nnetworks in Julia, Python and R Nov 27, 2023
  61. A plethora of datasets at your fingertips Nov 20, 2023
  62. A classifier that's very accurate (and deep) Nov 12, 2023
  63. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R Nov 5, 2023
  64. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting Oct 29, 2023
  65. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Oct 22, 2023
  66. Version v0.14.0 of nnetsauce for R and Python Oct 16, 2023
  67. A diffusion model: G2++ Oct 9, 2023
  68. Diffusion models in ESGtoolkit + announcements Oct 2, 2023
  69. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification) Sep 25, 2023
  70. (News from) forecasting in Python with ahead (progress bars and plots) Sep 18, 2023
  71. Forecasting in Python with ahead Sep 11, 2023
  72. Risk-neutralize simulations Sep 4, 2023
  73. Comparing cross-validation results using crossval_ml and boxplots Aug 27, 2023
  74. Reminder Apr 30, 2023
  75. Did you ask ChatGPT about who you are? Apr 16, 2023
  76. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks) Apr 2, 2023
  77. Simple interfaces to the forecasting API Nov 23, 2022
  78. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc. Nov 2, 2022
  79. Prediction intervals (not only) for Boosted Configuration Networks in Python Oct 5, 2022
  80. Boosted Configuration (neural) Networks Pt. 2 Sep 3, 2022
  81. Boosted Configuration (_neural_) Networks for classification Jul 21, 2022
  82. A Machine Learning workflow using Techtonique Jun 6, 2022
  83. Super Mario Bros © in the browser using PyScript May 8, 2022
  84. News from ESGtoolkit, ycinterextra, and nnetsauce Apr 4, 2022
  85. Explaining a Keras _neural_ network predictions with the-teller Mar 11, 2022
  86. New version of nnetsauce -- various quasi-randomized networks Feb 12, 2022
  87. A dashboard illustrating bivariate time series forecasting with `ahead` Jan 14, 2022
  88. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret Dec 20, 2021
  89. Forecasting with `ahead` (Python version) Dec 13, 2021
  90. Tuning and interpreting LSBoost Nov 15, 2021
  91. Time series cross-validation using `crossvalidation` (Part 2) Nov 7, 2021
  92. Fast and scalable forecasting with ahead::ridge2f Oct 31, 2021
  93. Automatic Forecasting with `ahead::dynrmf` and Ridge regression Oct 22, 2021
  94. Forecasting with `ahead` Oct 15, 2021
  95. Classification using linear regression Sep 26, 2021
  96. `crossvalidation` and random search for calibrating support vector machines Aug 6, 2021
  97. parallel grid search cross-validation using `crossvalidation` Jul 31, 2021
  98. `crossvalidation` on R-universe, plus a classification example Jul 23, 2021
  99. Documentation and source code for GPopt, a package for Bayesian optimization Jul 2, 2021
  100. Hyperparameters tuning with GPopt Jun 11, 2021
  101. A forecasting tool (API) with examples in curl, R, Python May 28, 2021
  102. Bayesian Optimization with GPopt Part 2 (save and resume) Apr 30, 2021
  103. Bayesian Optimization with GPopt Apr 16, 2021
  104. Compatibility of nnetsauce and mlsauce with scikit-learn Mar 26, 2021
  105. Explaining xgboost predictions with the teller Mar 12, 2021
  106. An infinity of time series models in nnetsauce Mar 6, 2021
  107. New activation functions in mlsauce's LSBoost Feb 12, 2021
  108. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce Dec 29, 2020
  109. A deeper learning architecture in nnetsauce Dec 18, 2020
  110. Classify penguins with nnetsauce's MultitaskClassifier Dec 11, 2020
  111. Bayesian forecasting for uni/multivariate time series Dec 4, 2020
  112. Generalized nonlinear models in nnetsauce Nov 28, 2020
  113. Boosting nonlinear penalized least squares Nov 21, 2020
  114. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates Nov 6, 2020
  115. NEWS Oct 30, 2020
  116. A glimpse into my PhD journey Oct 23, 2020
  117. Submitting R package to CRAN Oct 16, 2020
  118. Simulation of dependent variables in ESGtoolkit Oct 9, 2020
  119. Forecasting lung disease progression Oct 2, 2020
  120. New nnetsauce Sep 25, 2020
  121. Technical documentation Sep 18, 2020
  122. A new version of nnetsauce, and a new Techtonique website Sep 11, 2020
  123. Back next week, and a few announcements Sep 4, 2020
  124. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso) Jul 31, 2020
  125. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python) Jul 24, 2020
  126. nnetsauce version 0.5.0, randomized neural networks on GPU Jul 17, 2020
  127. Maximizing your tip as a waiter (Part 2) Jul 10, 2020
  128. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees Jul 3, 2020
  129. Announcements Jun 26, 2020
  130. Parallel AdaOpt classification Jun 19, 2020
  131. Comments section and other news Jun 12, 2020
  132. Maximizing your tip as a waiter Jun 5, 2020
  133. AdaOpt classification on MNIST handwritten digits (without preprocessing) May 29, 2020
  134. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R May 22, 2020
  135. AdaOpt May 15, 2020
  136. Custom errors for cross-validation using crossval::crossval_ml May 8, 2020
  137. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability May 1, 2020
  138. Encoding your categorical variables based on the response variable and correlations Apr 24, 2020
  139. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml Apr 17, 2020
  140. Grid search cross-validation using crossval Apr 10, 2020
  141. Documentation for the querier, a query language for Data Frames Apr 3, 2020
  142. Time series cross-validation using crossval Mar 27, 2020
  143. On model specification, identification, degrees of freedom and regularization Mar 20, 2020
  144. Import data into the querier (now on Pypi), a query language for Data Frames Mar 13, 2020
  145. R notebooks for nnetsauce Mar 6, 2020
  146. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification Feb 28, 2020
  147. Create a specific feed in your Jekyll blog Feb 21, 2020
  148. Git/Github for contributing to package development Feb 14, 2020
  149. Feedback forms for contributing Feb 7, 2020
  150. nnetsauce for R Jan 31, 2020
  151. A new version of nnetsauce (v0.3.1) Jan 24, 2020
  152. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0) Jan 17, 2020
  153. Search bar, new year 2020 Jan 10, 2020
  154. 2019 Recap, the nnetsauce, the teller and the querier Dec 20, 2019
  155. Understanding model interactions with the `teller` Dec 13, 2019
  156. Using the `teller` on a classifier Dec 6, 2019
  157. Benchmarking the querier's verbs Nov 29, 2019
  158. Composing the querier's verbs for data wrangling Nov 22, 2019
  159. Comparing and explaining model predictions with the teller Nov 15, 2019
  160. Tests for the significance of marginal effects in the teller Nov 8, 2019
  161. Introducing the teller Nov 1, 2019
  162. Introducing the querier Oct 25, 2019
  163. Prediction intervals for nnetsauce models Oct 18, 2019
  164. Using R in Python for statistical learning/data science Oct 11, 2019
  165. Model calibration with `crossval` Oct 4, 2019
  166. Bagging in the nnetsauce Sep 25, 2019
  167. Adaboost learning with nnetsauce Sep 18, 2019
  168. Change in blog's presentation Sep 4, 2019
  169. nnetsauce on Pypi Jun 5, 2019
  170. More nnetsauce (examples of use) May 9, 2019
  171. nnetsauce Mar 13, 2019
  172. crossval Mar 13, 2019
  173. test Mar 10, 2019

Categories | Top

Misc

  1. A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction
  2. Julia packaging at the command line
  3. A plethora of datasets at your fingertips
  4. Risk-neutralize simulations
  5. Comparing cross-validation results using crossval_ml and boxplots
  6. Reminder
  7. Did you ask ChatGPT about who you are?
  8. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  9. Boosted Configuration (_neural_) Networks for classification
  10. Super Mario Bros © in the browser using PyScript
  11. News from ESGtoolkit, ycinterextra, and nnetsauce
  12. Time series cross-validation using `crossvalidation` (Part 2)
  13. Fast and scalable forecasting with ahead::ridge2f
  14. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  15. Forecasting with `ahead`
  16. Documentation and source code for GPopt, a package for Bayesian optimization
  17. Hyperparameters tuning with GPopt
  18. A forecasting tool (API) with examples in curl, R, Python
  19. Bayesian Optimization with GPopt Part 2 (save and resume)
  20. Bayesian Optimization with GPopt
  21. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  22. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  23. NEWS
  24. A glimpse into my PhD journey
  25. Forecasting lung disease progression
  26. New nnetsauce
  27. Technical documentation
  28. A new version of nnetsauce, and a new Techtonique website
  29. Back next week, and a few announcements
  30. Maximizing your tip as a waiter (Part 2)
  31. New version of mlsauce, with Gradient Boosted randomized networks and stump decision trees
  32. Announcements
  33. Comments section and other news
  34. Maximizing your tip as a waiter
  35. Custom errors for cross-validation using crossval::crossval_ml
  36. Encoding your categorical variables based on the response variable and correlations
  37. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  38. Grid search cross-validation using crossval
  39. Time series cross-validation using crossval
  40. On model specification, identification, degrees of freedom and regularization
  41. Create a specific feed in your Jekyll blog
  42. Git/Github for contributing to package development
  43. Feedback forms for contributing
  44. Change in blog's presentation
  45. test

Python

  1. GLMNet in Python: Generalized Linear Models
  2. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  3. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  4. Chat with your tabular data in www.techtonique.net
  5. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  6. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  7. R and Python consoles + JupyterLite in www.techtonique.net
  8. Gradient-Boosting anything (alert: high performance)
  9. Benchmarking 30 statistical/Machine Learning models on the VN1 Forecasting -- Accuracy challenge
  10. Forecasting in Excel using Techtonique's Machine Learning APIs under the hood
  11. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  12. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  13. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  14. New versions of nnetsauce and ahead
  15. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  16. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  17. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  18. Copulas for uncertainty quantification in time series forecasting
  19. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  20. learningmachine for Python (new version)
  21. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  22. 10 uncertainty quantification methods in nnetsauce forecasting
  23. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  24. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  25. Automated hyperparameter tuning using any conformalized surrogate
  26. Recognizing handwritten digits with Ridge2Classifier
  27. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  28. Probability of receiving a loan; using learningmachine
  29. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  30. mlsauce's `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
  31. mlsauce's `v0.13.0`: taking into account inputs heterogeneity through clustering
  32. mlsauce's `v0.12.0`: prediction intervals for LSBoostRegressor
  33. learningmachine v1.1.2: for Python
  34. Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
  35. rtopy (v0.1.1): calling R functions in Python
  36. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  37. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  38. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  39. Tuning Machine Learning models with GPopt's new version Part 2
  40. Tuning Machine Learning models with GPopt's new version
  41. Subsampling continuous and discrete response variables
  42. DeepMTS, a Deep Learning Model for Multivariate Time Series
  43. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  44. A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction
  45. Diffusion models in Python with esgtoolkit (Part2)
  46. Diffusion models in Python with esgtoolkit
  47. Quasi-randomized nnetworks in Julia, Python and R
  48. A plethora of datasets at your fingertips
  49. A classifier that's very accurate (and deep)
  50. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  51. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  52. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  53. Version v0.14.0 of nnetsauce for R and Python
  54. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  55. (News from) forecasting in Python with ahead (progress bars and plots)
  56. Forecasting in Python with ahead
  57. Did you ask ChatGPT about who you are?
  58. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  59. Simple interfaces to the forecasting API
  60. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  61. Prediction intervals (not only) for Boosted Configuration Networks in Python
  62. A Machine Learning workflow using Techtonique
  63. Super Mario Bros © in the browser using PyScript
  64. News from ESGtoolkit, ycinterextra, and nnetsauce
  65. Explaining a Keras _neural_ network predictions with the-teller
  66. New version of nnetsauce -- various quasi-randomized networks
  67. A dashboard illustrating bivariate time series forecasting with `ahead`
  68. Forecasting with `ahead` (Python version)
  69. Tuning and interpreting LSBoost
  70. Classification using linear regression
  71. Documentation and source code for GPopt, a package for Bayesian optimization
  72. Hyperparameters tuning with GPopt
  73. A forecasting tool (API) with examples in curl, R, Python
  74. Bayesian Optimization with GPopt Part 2 (save and resume)
  75. Bayesian Optimization with GPopt
  76. Compatibility of nnetsauce and mlsauce with scikit-learn
  77. Explaining xgboost predictions with the teller
  78. An infinity of time series models in nnetsauce
  79. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  80. A deeper learning architecture in nnetsauce
  81. Generalized nonlinear models in nnetsauce
  82. Boosting nonlinear penalized least squares
  83. Technical documentation
  84. A new version of nnetsauce, and a new Techtonique website
  85. Back next week, and a few announcements
  86. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  87. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  88. nnetsauce version 0.5.0, randomized neural networks on GPU
  89. Maximizing your tip as a waiter (Part 2)
  90. Parallel AdaOpt classification
  91. Maximizing your tip as a waiter
  92. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  93. AdaOpt
  94. Documentation+Pypi for the `teller`, a model-agnostic tool for Machine Learning explainability
  95. Encoding your categorical variables based on the response variable and correlations
  96. Documentation for the querier, a query language for Data Frames
  97. Import data into the querier (now on Pypi), a query language for Data Frames
  98. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  99. A new version of nnetsauce (v0.3.1)
  100. Using R in Python for statistical learning/data science
  101. nnetsauce on Pypi

QuasiRandomizedNN

  1. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  2. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  3. My presentation at ISF 2024 conference (slides with nnetsauce probabilistic forecasting news)
  4. 10 uncertainty quantification methods in nnetsauce forecasting
  5. Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks
  6. Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks
  7. Automated hyperparameter tuning using any conformalized surrogate
  8. Recognizing handwritten digits with Ridge2Classifier
  9. A plethora of datasets at your fingertips Part3: how many times do couples cheat on each other?
  10. nnetsauce's introduction as of 2024-02-11 (new version 0.17.0)
  11. DeepMTS, a Deep Learning Model for Multivariate Time Series
  12. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  13. Quasi-randomized nnetworks in Julia, Python and R
  14. A classifier that's very accurate (and deep)
  15. AutoML in nnetsauce (randomized and quasi-randomized nnetworks) Pt.2: multivariate time series forecasting
  16. AutoML in nnetsauce (randomized and quasi-randomized nnetworks)
  17. Version v0.14.0 of nnetsauce for R and Python
  18. An infinity of time series forecasting models in nnetsauce (Part 2 with uncertainty quantification)
  19. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  20. New version of nnetsauce -- various quasi-randomized networks
  21. Tuning and interpreting LSBoost
  22. Classification using linear regression
  23. An infinity of time series models in nnetsauce
  24. A deeper learning architecture in nnetsauce
  25. Classify penguins with nnetsauce's MultitaskClassifier
  26. Bayesian forecasting for uni/multivariate time series
  27. New nnetsauce
  28. Technical documentation
  29. A new version of nnetsauce, and a new Techtonique website
  30. Back next week, and a few announcements
  31. nnetsauce version 0.5.0, randomized neural networks on GPU
  32. R notebooks for nnetsauce
  33. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  34. Feedback forms for contributing
  35. nnetsauce for R
  36. A new version of nnetsauce (v0.3.1)
  37. 2019 Recap, the nnetsauce, the teller and the querier
  38. Prediction intervals for nnetsauce models
  39. Bagging in the nnetsauce
  40. Adaboost learning with nnetsauce
  41. nnetsauce on Pypi
  42. More nnetsauce (examples of use)
  43. nnetsauce

R

  1. GLMNet in Python: Generalized Linear Models
  2. Gradient-Boosting anything (alert: high performance): Part4, Time series forecasting
  3. Predictive scenarios simulation in R, Python and Excel using Techtonique API
  4. Chat with your tabular data in www.techtonique.net
  5. Gradient-Boosting anything (alert: high performance): Part3, Histogram-based boosting
  6. R editor and SQL console (in addition to Python editors) in www.techtonique.net
  7. R and Python consoles + JupyterLite in www.techtonique.net
  8. Gradient-Boosting anything (alert: high performance): Part2, R version
  9. Gradient-Boosting anything (alert: high performance)
  10. Automated random variable distribution inference using Kullback-Leibler divergence and simulating best-fitting distribution
  11. Techtonique web app for data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
  12. Parallel for loops (Map or Reduce) + New versions of nnetsauce and ahead
  13. Adaptive (online/streaming) learning with uncertainty quantification using Polyak averaging in learningmachine
  14. New versions of nnetsauce and ahead
  15. Prediction sets and prediction intervals for conformalized Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  16. Quick/automated R package development workflow (assuming you're using macOS or Linux) Part2
  17. R package development workflow (assuming you're using macOS or Linux)
  18. A new method for deriving a nonparametric confidence interval for the mean
  19. Conformalized adaptive (online/streaming) learning using learningmachine in Python and R
  20. Bayesian (nonlinear) adaptive learning
  21. Auto XGBoost, Auto LightGBM, Auto CatBoost, Auto GradientBoosting
  22. Forecasting uncertainty: sequential split conformal prediction + Block bootstrap (web app)
  23. learningmachine v2.0.0: Machine Learning with explanations and uncertainty quantification
  24. Forecasting the Economy
  25. A detailed introduction to Deep Quasi-Randomized 'neural' networks
  26. Probability of receiving a loan; using learningmachine
  27. mlsauce's `v0.18.2`: various examples and benchmarks with dimension reduction
  28. Conformalized predictive simulations for univariate time series on more than 250 data sets
  29. learningmachine v1.0.0: prediction intervals around the probability of the event 'a tumor being malignant'
  30. Multiple examples of Machine Learning forecasting with ahead
  31. rtopy (v0.1.1): calling R functions in Python
  32. ahead forecasting (v0.10.0): fast time series model calibration and Python plots
  33. A classifier that's very accurate (and deep) Pt.2: there are > 90 classifiers in nnetsauce
  34. learningmachine: prediction intervals for conformalized Kernel ridge regression and Random Forest
  35. A plethora of datasets at your fingertips Part2: how many times do couples cheat on each other? Descriptive analytics, interpretability and prediction intervals using conformal prediction
  36. Diffusion models in Python with esgtoolkit (Part2)
  37. Diffusion models in Python with esgtoolkit
  38. Quasi-randomized nnetworks in Julia, Python and R
  39. A plethora of datasets at your fingertips
  40. A classifier that's very accurate (and deep)
  41. mlsauce version 0.8.10: Statistical/Machine Learning with Python and R
  42. Version v0.14.0 of nnetsauce for R and Python
  43. A diffusion model: G2++
  44. Diffusion models in ESGtoolkit + announcements
  45. Risk-neutralize simulations
  46. Comparing cross-validation results using crossval_ml and boxplots
  47. Did you ask ChatGPT about who you are?
  48. A new version of nnetsauce (randomized and quasi-randomized 'neural' networks)
  49. Simple interfaces to the forecasting API
  50. A web application for forecasting in Python, R, Ruby, C#, JavaScript, PHP, Go, Rust, Java, MATLAB, etc.
  51. Boosted Configuration (neural) Networks Pt. 2
  52. Boosted Configuration (_neural_) Networks for classification
  53. News from ESGtoolkit, ycinterextra, and nnetsauce
  54. New version of nnetsauce -- various quasi-randomized networks
  55. A dashboard illustrating bivariate time series forecasting with `ahead`
  56. Hundreds of Statistical/Machine Learning models for univariate time series, using ahead, ranger, xgboost, and caret
  57. Time series cross-validation using `crossvalidation` (Part 2)
  58. Fast and scalable forecasting with ahead::ridge2f
  59. Automatic Forecasting with `ahead::dynrmf` and Ridge regression
  60. Forecasting with `ahead`
  61. `crossvalidation` and random search for calibrating support vector machines
  62. parallel grid search cross-validation using `crossvalidation`
  63. `crossvalidation` on R-universe, plus a classification example
  64. A forecasting tool (API) with examples in curl, R, Python
  65. An infinity of time series models in nnetsauce
  66. New activation functions in mlsauce's LSBoost
  67. 2020 recap, Gradient Boosting, Generalized Linear Models, AdaOpt with nnetsauce and mlsauce
  68. Classify penguins with nnetsauce's MultitaskClassifier
  69. Bayesian forecasting for uni/multivariate time series
  70. Boosting nonlinear penalized least squares
  71. Statistical/Machine Learning explainability using Kernel Ridge Regression surrogates
  72. Submitting R package to CRAN
  73. Simulation of dependent variables in ESGtoolkit
  74. Forecasting lung disease progression
  75. Explainable 'AI' using Gradient Boosted randomized networks Pt2 (the Lasso)
  76. LSBoost: Explainable 'AI' using Gradient Boosted randomized networks (with examples in R and Python)
  77. nnetsauce version 0.5.0, randomized neural networks on GPU
  78. Maximizing your tip as a waiter (Part 2)
  79. Maximizing your tip as a waiter
  80. AdaOpt classification on MNIST handwritten digits (without preprocessing)
  81. AdaOpt (a probabilistic classifier based on a mix of multivariable optimization and nearest neighbors) for R
  82. Custom errors for cross-validation using crossval::crossval_ml
  83. Encoding your categorical variables based on the response variable and correlations
  84. Linear model, xgboost and randomForest cross-validation using crossval::crossval_ml
  85. Grid search cross-validation using crossval
  86. Time series cross-validation using crossval
  87. On model specification, identification, degrees of freedom and regularization
  88. R notebooks for nnetsauce
  89. Version 0.4.0 of nnetsauce, with fruits and breast cancer classification
  90. Feedback forms for contributing
  91. nnetsauce for R
  92. ESGtoolkit, a tool for Monte Carlo simulation (v0.2.0)
  93. Using R in Python for statistical learning/data science
  94. Model calibration with `crossval`
  95. crossval