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A new release of pins is available on CRAN today. This release adds support to t...
The term "federated learning" was coined to describe a form of distributed model...
Kullback-Leibler divergence is not just used to train variational autoencoders o...
Broadcasting, as done by Python's scientific computing library NumPy, involves d...
TensorFlow 2.1, released last week, allows for mixed-precision training, making ...
Differential Privacy guarantees that results of a database query are basically i...
TensorFlow Hub is a library for the publication, discovery, and consumption of r...
Continuing our tour of applications of TensorFlow Probability (TFP), after Bayes...
Looking for materials to get started with deep learning from R? This post presen...
In a Bayesian neural network, layer weights are distributions, not tensors. Usin...
Part of the r-tensorflow ecosystem, tfprobability is an R wrapper to TensorFlow ...
Is society ready to deal with challenges brought about by artificially-generated...
TensorFlow 2.0 was finally released last week. As R users we have two kinds of q...
TensorFlow Probability, and its R wrapper tfprobability, provide Markov Chain Mo...