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) tas…
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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 …
Image segmentation with U-Net
In image segmentation, every pixel of an image is assigned a class. Depending on the application, classes could be different cell types; or the task c…
Modeling censored data with tfprobability
In this post we use tfprobability, the R interface to TensorFlow Probability, to model censored data. Again, the exposition is inspired by the treatme…
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…
Dynamic linear models with tfprobability
Previous posts featuring tfprobability - the R interface to TensorFlow Probability - have focused on enhancements to deep neural networks (e.g., intro…
Adding uncertainty estimates to Keras models with tfprobability
As of today, there is no mainstream road to obtaining uncertainty estimates from neural networks. All that can be said is that, normally, approaches t…
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…
