Posts

Please allow me to introduce myself: Torch for R

Today, we are excited to introduce torch, an R package that allows you to use Py...

Introducing sparklyr.flint: A time-series extension for...

We are pleased to announce that sparklyr.flint, a sparklyr extension for analyzi...

An introduction to weather forecasting with deep learning

A few weeks ago, we showed how to forecast chaotic dynamical systems with deep l...

Training ImageNet with R

This post explores how to train large datasets with TensorFlow and R. Specifical...

Deepfake detection challenge from R

A couple of months ago, Amazon, Facebook, Microsoft, and other contributors init...

FNN-VAE for noisy time series forecasting

In the last part of this mini-series on forecasting with false nearest neighbors...

State-of-the-art NLP models from R

Nowadays, Microsoft, Google, Facebook, and OpenAI are sharing lots of state-of-t...

Parallelized sampling using exponential variates

How can the seemingly iterative process of weighted sampling without replacement...

Time series prediction with FNN-LSTM

In a recent post, we showed how an LSTM autoencoder, regularized by false neares...

sparklyr 1.3: Higher-order Functions, Avro and Custom S...

Sparklyr 1.3 is now available, featuring exciting new functionalities such as in...

Deep attractors: Where deep learning meets chaos

In nonlinear dynamics, when the state space is thought to be multidimensional bu...

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...

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