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Tabular Data

Blog Posts tagged Tabular Data#

mall 0.2.0

The mall 0.2.0 update for R and Python introduces support for external LLM providers like OpenAI and Gemini. This version also features parallel processing for R users, the ability to run NLP on string vectors in Python, and a brand new cheatsheet

Edgar Ruiz

Aug 19, 2025

Artificial Intelligence Machine Learning Ai LLM Polars Natural Language Processing Tabular Data

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Introducing mall for R...and Python

We are proud to introduce the {mall} package. With {mall}, you can use a local LLM to run NLP operations across a data frame. (sentiment, summarization, translation, etc). {mall} has been simultaneously released to CRAN and PyPi (as an extension to Polars)

Edgar Ruiz

Oct 30, 2024

Artificial Intelligence Machine Learning Ai LLM Polars Natural Language Processing Tabular Data

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Five ways to do least squares (with torch)

Five ways to do least squares (with torch)

Get to know torch's linalg module, all while learning about different ways to do least-squares regression from scratch. This post is a condensed version of the corresponding chapter in the forthcoming book, Deep Learning and Scientific Computing with R torch, to be published by CRC Press

Sigrid Keydana

Oct 13, 2022

Machine Learning Best Practices Ai Torch Concepts Tabular Data

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torch, tidymodels, and high-energy physics

torch, tidymodels, and high-energy physics

Today we introduce tabnet, a torch implementation of "TabNet: Attentive Interpretable Tabular Learning" that is fully integrated with the tidymodels framework. Per se, already, tabnet was designed to require very little data pre-processing; thanks to tidymodels, hyperparameter tuning (so often cumbersome in deep learning) becomes convenient and even, fun!

Sigrid Keydana

Feb 11, 2021

tidymodels Machine Learning Ai Torch Tabular Data

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torch for tabular data

How not to die from poisonous mushrooms. Also: How to use torch for deep learning on tabular data, including a mix of categorical and numerical features

Sigrid Keydana

Nov 3, 2020

Machine Learning Ai Torch Tabular Data

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TensorFlow feature columns: Transforming your data recipes-style

TensorFlow feature columns: Transforming your data recipes-style

TensorFlow feature columns provide useful functionality for preprocessing categorical data and chaining transformations, like bucketization or feature crossing. From R, we use them in popular "recipes" style, creating and subsequently refining a feature specification. In this post, we show how using feature specs frees cognitive resources and lets you focus on what you really want to accomplish. What's more, because of its elegance, feature-spec code reads nice and is fun to write as well

Daniel Falbel, Sigrid Keydana

Jul 9, 2019

keras3 Machine Learning Ai Tensorflow Keras Tabular Data

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Entity embeddings for fun and profit

Embedding layers are not just useful when working with language data. As "entity embeddings", they've recently become famous for applications on tabular, small-scale data. In this post, we exemplify two possible use cases, also drawing attention to what not to expect

Sigrid Keydana

Nov 26, 2018

keras3 Machine Learning Ai Tensorflow Keras Tabular Data

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Collaborative filtering with embeddings

Embeddings are not just for use in natural language processing. Here we apply embeddings to a common task in collaborative filtering - predicting user ratings - and on our way, strive for a better understanding of what an embedding layer really does

Sigrid Keydana

Sep 26, 2018

keras3 Machine Learning Ai Tensorflow Keras Tabular Data

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Deep Learning for Cancer Immunotherapy

The aim of this post is to illustrate how deep learning is being applied in cancer immunotherapy (Immuno-oncology or Immunooncology) - a cancer treatment strategy, where the aim is to utilize the cancer patient's own immune system to fight the cancer

Leon Eyrich Jessen

Jan 29, 2018

keras3 Machine Learning Ai Tensorflow Keras Tabular Data

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Deep Learning With Keras To Predict Customer Churn

Deep Learning With Keras To Predict Customer Churn

Using Keras to predict customer churn based on the IBM Watson Telco Customer Churn dataset. We also demonstrate using the lime package to help explain which features drive individual model predictions. In addition, we use three new packages to assist with Machine Learning: recipes for preprocessing, rsample for sampling data and yardstick for model metrics

Matt Dancho

Jan 11, 2018

keras3 Machine Learning Ai Tensorflow Keras Tabular Data Explainability