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jluraschi

Javier Luraschi

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Spark and MLflow - rstudio::conf
Slides for "Updates on Spark, MLflow, and the broader ML ecosystem" at rstudio::conf 2020.
Strange Downloads - rstudio::conf
'pins' package lighting talk in rstudio::cong
Linux Foundation and sparklyr
TAC meeting presenting sparklyr to Linux Foundation AI.
A huge nomnoml diagram
Briefing on the Modern ML Stack with R
"Briefing on the Modern ML Stack with R" for Spark and AI Summit Europe 2019.
Data workflows in RStudio Connect with pins
"Data workflows in RStudio Connect with pins" for Procogia Roadshow.
Reusing Tidy Datasets with pins
A use case for the pins package showcasing how to reuse tidy datasets while doing data analysis.
Cluster Computing Made Easy with Spark and R
This talk should be of interest to new users that are unfamiliar to cluster computing, intermediate users that have used Apache Spark with R and advanced users interested in learning the latest best practices and features available in Spark with R.
Scaling Spark with Streams and Arrow
In this talk you will learn how to analyze large datasets, in realtime, from R using Apache Spark through the sparklyr R package. We will briefly introduce Apache Spark, sparklyr and give you a few examples and resources to use dplyr, broom, MLlib and mleap with Spark, from R. This talk will introduce Structured Streaming in Spark using R, we will discuss various use cases and the supported tools and workflows. You will also learn how to easily configure Apache Arrow with R on Apache Spark, which will allow you to gain speed improvements and expand the scope of your data science workflows; for instance, by enabling data to be efficiently transferred between your local environment and Apache Spark.
Interactive Voxels Rendering in R
A brief notebook describing how to use the voxels package.
Interactive Voxel Rendering in R
Lighting talk presenting voxels to interactive render voxel worlds in R
Introduction to Deep Learning - SDSS 2019
Instructor(s): Kevin Kuo, RStudio; Javier Luraschi, RStudio This is a practical introduction to neural networks with interactive coding exercises in R. We provide an overview of different types of neural network architectures and how they can be applied in a variety of applications.
A Broad Packages Talk - Seattle useR
A Broad Packages Talk - Seattle useR Presents various R packages I've been authored or co-authored around scaling, visualization, deep learning and interoperability. https://www.meetup.com/Seattle-useR/events/260225995/
Spark Summit - R at Scale with Arrow on Spark
Running R at Scale with Apache Arrow on Spark - Spark Summit 2019
Scaling R with Spark - rstudio::conf
Slides for "Scaling R with Spark" rstudio::conf 2019 talk.
MLflow with R - Seattle R Meetup
Slides for MLFlow with R for Seattle R Users meetup.
spark_apply() profile under sparklyr and arrow
spark_apply() profile under sparklyr and arrow for https://github.com/rstudio/sparklyr/pull/1611
MLflow with R - Meetup
MLflow with R - Bay Area MLflow Meetup
Distributed Data Science with sparklyr - JSM 2018
Slides for the "Distributed Data Science with sparklyr" talk in JSM 2018 under the "Cloud and Distributed Computing for Statisticians — Invited Papers" track.
R2D3 Sunburst
Sunburst visualization published using R2D3.
An R profile over NYC flights for sparklyr's spark_apply
An R profile over NYC flights for sparklyr's spark_apply using a subset of this dataset.
Deep Learning in JavaScript using R
An rmarkdown presentation using kerasjs to render a deep learning model into a slide all built from R.
sparklyr - Active Bugs 2017
sparklyr - extensions webinar - 2017
Notebook with some of the resources used in the sparklyr extensions webinar.
sparklyr - spark summit 2017
Spark Summit 2017 slides for the "SPARKLYR: RECAP, UPDATES, AND USE CASES" session.
RStudio Images
A list of all RStudio images
sparklyr - Active Bugs 2016
Analyzing 4 Billion HTML tags in R and Spark
Using sparklyr we analyze 4 billion html tags and attributes in Apache Spark to discover what are the most used keywords in the world and what the most used javascript libraries are.
R and Spark - rstudio::conf
R and Spark - rstudio::conf - https://www.rstudio.com/conference/