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Blogroll | Top
List of posts by date of publication | Top
- 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
Python
- 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