<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Niels van der Velden Blog</title><description>Posts on machine learning, proteomics, and software projects.</description><link>https://www.nielsvandervelden.com/</link><item><title>Protein encodings</title><link>https://www.nielsvandervelden.com/blog/protein-encoding-for-deep-learning-models/</link><guid isPermaLink="true">https://www.nielsvandervelden.com/blog/protein-encoding-for-deep-learning-models/</guid><description>In order to use protein sequences as inputs for deep learning models they need to be transformed into numerical descriptors. This notebook highlights four different feature encodings for protein sequences.</description><pubDate>Wed, 15 Jun 2022 00:00:00 GMT</pubDate></item><item><title>Predicting Titanic passenger survival using tidymodels</title><link>https://www.nielsvandervelden.com/blog/predicting-titanic-passenger-survival-using-tidymodels/</link><guid isPermaLink="true">https://www.nielsvandervelden.com/blog/predicting-titanic-passenger-survival-using-tidymodels/</guid><description>How I built and tuned a machine-learning model in R using tidymodels, engineered additional features, and used Optuna via Python for tuning.</description><pubDate>Wed, 23 Feb 2022 00:00:00 GMT</pubDate></item><item><title>Tune R tidymodels with the Python optuna package</title><link>https://www.nielsvandervelden.com/blog/tune-tidymodels-using-the-optuna-python-library/</link><guid isPermaLink="true">https://www.nielsvandervelden.com/blog/tune-tidymodels-using-the-optuna-python-library/</guid><description>A comparison of direct grid search in R versus tuning with Optuna through reticulate, applied to tidymodels workflows.</description><pubDate>Wed, 16 Feb 2022 00:00:00 GMT</pubDate></item><item><title>Optuna Samplers</title><link>https://www.nielsvandervelden.com/blog/optuna-samplers/</link><guid isPermaLink="true">https://www.nielsvandervelden.com/blog/optuna-samplers/</guid><description>An interactive explanation of Optuna samplers and why they are effective for hyperparameter optimization in ML workflows.</description><pubDate>Mon, 07 Feb 2022 00:00:00 GMT</pubDate></item><item><title>Editable DataTables in R shiny using SQL</title><link>https://www.nielsvandervelden.com/blog/editable-datatables-in-r-shiny-using-sql/</link><guid isPermaLink="true">https://www.nielsvandervelden.com/blog/editable-datatables-in-r-shiny-using-sql/</guid><description>A tutorial showing Add/Edit/Copy/Delete functionality in Shiny DataTables backed by a local SQL database.</description><pubDate>Wed, 14 Aug 2019 00:00:00 GMT</pubDate></item></channel></rss>