RStudio AI

sparklyr 1.5: better dplyr interface, more sdf_* functi...

Unlike all three previous sparklyr releases, the recent release of sparklyr 1.5 ...

tfprobability 0.8 on CRAN: Now how can you use it?

Part of the r-tensorflow ecosystem, tfprobability is an R wrapper to TensorFlow ...

Tadpoles on TensorFlow: Hierarchical partial pooling wi...

This post is a first introduction to MCMC modeling with tfprobability, the R int...

Hierarchical partial pooling, continued: Varying slopes...

This post builds on our recent introduction to multi-level modeling with tfproba...

Adding uncertainty estimates to Keras models with tfpro...

As of today, there is no mainstream road to obtaining uncertainty estimates from...

Dynamic linear models with tfprobability

Previous posts featuring tfprobability - the R interface to TensorFlow Probabili...

TensorFlow feature columns: Transforming your data reci...

TensorFlow feature columns provide useful functionality for preprocessing catego...

Modeling censored data with tfprobability

In this post we use tfprobability, the R interface to TensorFlow Probability, to...

Image segmentation with U-Net

In image segmentation, every pixel of an image is assigned a class. Depending on...

So, how come we can use TensorFlow from R?

Have you ever wondered why you can call TensorFlow - mostly known as a Python fr...

BERT from R

A deep learning model - BERT from Google AI Research - has yielded state-of-the-...

On leapfrogs, crashing satellites, and going nuts: A ve...

TensorFlow Probability, and its R wrapper tfprobability, provide Markov Chain Mo...

TensorFlow 2.0 is here - what changes for R users?

TensorFlow 2.0 was finally released last week. As R users we have two kinds of q...

Innocent unicorns considered harmful? How to experiment...

Is society ready to deal with challenges brought about by artificially-generated...

torch 0.2.0 - Initial JIT support and many bug fixes

The torch 0.2.0 release includes many bug fixes and some nice new features like ...

Variational convnets with tfprobability

In a Bayesian neural network, layer weights are distributions, not tensors. Usin...

Getting started with Keras from R - the 2020 edition

Looking for materials to get started with deep learning from R? This post presen...

Gaussian Process Regression with tfprobability

Continuing our tour of applications of TensorFlow Probability (TFP), after Bayes...

tfhub: R interface to TensorFlow Hub

TensorFlow Hub is a library for the publication, discovery, and consumption of r...

Differential Privacy with TensorFlow

Differential Privacy guarantees that results of a database query are basically i...

First experiments with TensorFlow mixed-precision training

TensorFlow 2.1, released last week, allows for mixed-precision training, making ...

NumPy-style broadcasting for R TensorFlow users

Broadcasting, as done by Python's scientific computing library NumPy, involves d...

Infinite surprise - the iridescent personality of Kullb...

Kullback-Leibler divergence is not just used to train variational autoencoders o...

Introducing: The RStudio AI Blog

This blog just got a new title: RStudio AI Blog. We explain why.

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