Category: Keras

Broadcasting, as done by Python’s scientific computing library NumPy, involves dynamically extending shapes so that arrays of different sizes may be passed to operations that expect conformity – such as...continue reading.

TensorFlow 2.1, released last week, allows for mixed-precision training, making use of the Tensor Cores available in the most recent NVidia GPUs. In this post, we report first experimental results...continue reading.

Differential Privacy guarantees that results of a database query are basically independent of the presence in the data of a single individual. Applied to machine learning, we expect that no...continue reading.

TensorFlow Hub is a library for the publication, discovery, and consumption of reusable parts of machine learning models. A module is a self-contained piece of a TensorFlow graph, along with...continue reading.

Continuing our tour of applications of TensorFlow Probability (TFP), after Bayesian Neural Networks, Hamiltonian Monte Carlo and State Space Models, here we show an example of Gaussian Process Regression. In...continue reading.

Looking for materials to get started with deep learning from R? This post presents useful tutorials, guides, and background documentation on the new TensorFlow for R website. Advanced users will...continue reading.

In a Bayesian neural network, layer weights are distributions, not tensors. Using tfprobability, the R wrapper to TensorFlow Probability, we can build regular Keras models that have probabilistic layers, and...continue reading.

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...continue reading.

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...continue reading.

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?...continue reading.

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...continue reading.

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...continue reading.

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...continue reading.

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 could be binary, as in...continue reading.

In this post we use tfprobability, the R interface to TensorFlow Probability, to model censored data. Again, the exposition is inspired by the treatment of this topic in Richard McElreath’s...continue reading.

TensorFlow feature columns provide useful functionality for preprocessing categorical data and chaining transformations, like bucketization or feature crossing. From R, we use them in popular “recipes” style, creating and subsequently...continue reading.

Previous posts featuring tfprobability – the R interface to TensorFlow Probability – have focused on enhancements to deep neural networks (e.g., introducing Bayesian uncertainty estimates) and fitting hierarchical models with...continue reading.

As of today, there is no mainstream road to obtaining uncertainty estimates from neural networks. All that can be said is that, normally, approaches tend to be Bayesian in spirit,...continue reading.