Differential Privacy with TensorFlow
Differential Privacy guarantees that results of a database query are basically independent of the presence in the data of a single individual. Applied…
Tag: TensorFlow
tfhub: R interface to TensorFlow Hub
TensorFlow Hub is a library for the publication, discovery, and consumption of reusable parts of machine learning models. A module is a self-contained…
So, how come we can use TensorFlow from R?
Have you ever wondered why you can call TensorFlow - mostly known as a Python framework - from R? If not - that's how it should be, as the R packages …
TensorFlow feature columns: Transforming your data recipes-style
TensorFlow feature columns provide useful functionality for preprocessing categorical data and chaining transformations, like bucketization or feature…
Hierarchical partial pooling, continued: Varying slopes models with TensorFlow Probability
This post builds on our recent introduction to multi-level modeling with tfprobability, the R wrapper to TensorFlow Probability. We show how to pool n…
Tadpoles on TensorFlow: Hierarchical partial pooling with tfprobability
This post is a first introduction to MCMC modeling with tfprobability, the R interface to TensorFlow Probability (TFP). Our example is a multi-level m…
Experimenting with autoregressive flows in TensorFlow Probability
Continuing from the recent introduction to bijectors in TensorFlow Probability (TFP), this post brings autoregressivity to the table. Using TFP throug…
Getting into the flow: Bijectors in TensorFlow Probability
Normalizing flows are one of the lesser known, yet fascinating and successful architectures in unsupervised deep learning. In this post we provide a b…
Discrete Representation Learning with VQ-VAE and TensorFlow Probability
Mostly when thinking of Variational Autoencoders (VAEs), we picture the prior as an isotropic Gaussian. But this is by no means a necessity. The Vecto…
Getting started with TensorFlow Probability from R
TensorFlow Probability offers a vast range of functionality ranging from distributions over probabilistic network layers to probabilistic inference. I…
