pins 0.4: Versioning
A new release of pins is available on CRAN today. This release adds support to time travel across dataset versions, which improves collaboration and protects your code from breaking when remote resources change unexpectedly.
Artificial Intelligence
Category Added in a WPeMatico Campaign
BERT from R
A deep learning model – BERT from Google AI Research – has yielded state-of-the-art results in a wide variety of Natural Language Processing (NLP) tasks. In this tutorial, we will show how to load and train the BERT model from R, using Keras.
sparklyr 1.2: Foreach, Spark 3.0 and Databricks Connect
A new sparklyr release is now available. This sparklyr 1.2 release features new functionalities such as support for Databricks Connect, a Spark backend for the ‘foreach’ package, inter-op improvements for working with Spark 3.0 preview, as well as a number of bug fixes and improvements addressing user-visible pain points.
sparklyr 1.2: Foreach, Spark 3.0 and Databricks Connect Read More »
On leapfrogs, crashing satellites, and going nuts: A very first conceptual introduction to Hamiltonian Monte Carlo
TensorFlow Probability, and its R wrapper tfprobability, provide Markov Chain Monte Carlo (MCMC) methods that were used in a number of recent posts on this blog. These posts were directed to users already comfortable with the method, and terminology, per se, which readers mainly interested in deep learning won’t necessarily be. Here we try to
Towards privacy: Encrypted deep learning with Syft and Keras
Deep learning need not be irreconcilable with privacy protection. Federated learning enables on-device, distributed model training; encryption keeps model and gradient updates private; differential privacy prevents the training data from leaking. As of today, private and secure deep learning is an emerging technology. In this post, we introduce Syft, an open-source framework that integrates with
Towards privacy: Encrypted deep learning with Syft and Keras Read More »
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 questions. First, will my keras code still run? And second, what is it that changes? In this post, we answer both and, then, give a tour of exciting new developments in the r-tensorflow ecosystem.
TensorFlow 2.0 is here – what changes for R users? Read More »
Hacking deep learning: model inversion attack by example
Compared to other applications, deep learning models might not seem too likely as victims of privacy attacks. However, methods exist to determine whether an entity was used in the training set (an adversarial attack called member inference), and techniques subsumed under “model inversion” allow to reconstruct raw data input given just model output (and sometimes,
Hacking deep learning: model inversion attack by example Read More »
Innocent unicorns considered harmful? How to experiment with GPT-2 from R
Is society ready to deal with challenges brought about by artificially-generated information – fake images, fake videos, fake text? While this post won’t answer that question, it should help form an opinion on the threat exerted by fake text as of this writing, autumn 2019. We introduce gpt2, an R package that wraps OpenAI’s public
Innocent unicorns considered harmful? How to experiment with GPT-2 from R Read More »
Easy PixelCNN with tfprobability
PixelCNN is a deep learning architecture – or bundle of architectures – designed to generate highly realistic-looking images. To use it, no reverse-engineering of arXiv papers or search for reference implementations is required: TensorFlow Probability and its R wrapper, tfprobability, now include a PixelCNN distribution that can be used to train a straightforwardly-defined neural network
tfprobability 0.8 on CRAN: Now how can you use it?
Part of the r-tensorflow ecosystem, tfprobability is an R wrapper to TensorFlow Probability, the Python probabilistic programming framework developed by Google. We take the occasion of tfprobability’s acceptance on CRAN to give a high-level introduction, highlighting interesting use cases and applications.
tfprobability 0.8 on CRAN: Now how can you use it? Read More »