AI

Easy PixelCNN with tfprobability

PixelCNN is a deep learning architecture - or bundle of architectures - designed...

Hacking deep learning: model inversion attack by example

Compared to other applications, deep learning models might not seem too likely a...

Towards privacy: Encrypted deep learning with Syft and ...

Deep learning need not be irreconcilable with privacy protection. Federated lear...

sparklyr 1.2: Foreach, Spark 3.0 and Databricks Connect

A new sparklyr release is now available. This sparklyr 1.2 release features new ...

pins 0.4: Versioning

A new release of pins is available on CRAN today. This release adds support to t...

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...

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