Posts

A first look at federated learning with TensorFlow

The term "federated learning" was coined to describe a form of distributed model...

Introducing: The RStudio AI Blog

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

Infinite surprise - the iridescent personality of Kullb...

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

NumPy-style broadcasting for R TensorFlow users

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

First experiments with TensorFlow mixed-precision training

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

Differential Privacy with TensorFlow

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

tfhub: R interface to TensorFlow Hub

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

Gaussian Process Regression with tfprobability

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

Getting started with Keras from R - the 2020 edition

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

Variational convnets with tfprobability

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

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

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

Innocent unicorns considered harmful? How to experiment...

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

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...

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

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

BERT from R

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

So, how come we can use TensorFlow from R?

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

Image segmentation with U-Net

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

Modeling censored data with tfprobability

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

TensorFlow feature columns: Transforming your data reci...

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

Dynamic linear models with tfprobability

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

Adding uncertainty estimates to Keras models with tfpro...

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

Hierarchical partial pooling, continued: Varying slopes...

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

Tadpoles on TensorFlow: Hierarchical partial pooling wi...

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

This data set helps researchers spot harmful stereotype...

AI models are riddled with culturally specific biases. A new data set, called SH...

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