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.
This post builds on our recent introduction to multi-level modeling with tfprobability, the R wrapper to TensorFlow Probability. We show how to pool not just mean values (“intercepts”), but also...continue reading.
This post is a first introduction to MCMC modeling with tfprobability, the R interface to TensorFlow Probability (TFP). Our example is a multi-level model describing tadpole mortality, which may be...continue reading.
DALEX is a set of tools for explanation, exploration and debugging of predictive models. The nice thing about it is that it can be easily connected to different model factories....continue reading.
Continuing from the recent introduction to bijectors in TensorFlow Probability (TFP), this post brings autoregressivity to the table. Using TFP through the new R package tfprobability, we look at the...continue reading.
Sometimes in deep learning, architecture design and hyperparameter tuning pose substantial challenges. Using Auto-Keras, none of these is needed: We start a search procedure and extract the best-performing model. This...continue reading.
Normalizing flows are one of the lesser known, yet fascinating and successful architectures in unsupervised deep learning. In this post we provide a basic introduction to flows using tfprobability, an...continue reading.
Not everybody who wants to get into deep learning has a strong background in math or programming. This post elaborates on a concepts-driven, abstraction-based way to learn what it’s all...continue reading.
Sometimes, deep learning is seen – and welcomed – as a way to avoid laborious preprocessing of data. However, there are cases where preprocessing of sorts does not only help...continue reading.
Mostly when thinking of Variational Autoencoders (VAEs), we picture the prior as an isotropic Gaussian. But this is by no means a necessity. The Vector Quantised Variational Autoencoder (VQ-VAE) described...continue reading.
TensorFlow Probability offers a vast range of functionality ranging from distributions over probabilistic network layers to probabilistic inference. It works seamlessly with core TensorFlow and (TensorFlow) Keras. In this post,...continue reading.
As shown in a previous post, naming and locating a single object in an image is a task that may be approached in a straightforward way. This is not the...continue reading.
In this post we will return to the Pitchfork music data and use recurrent neural networks (a “deep learning” technique) to automatically generate band names.The DataFor this analysis, we will...continue reading.
Embedding layers are not just useful when working with language data. As “entity embeddings”, they’ve recently become famous for applications on tabular, small-scale data. In this post, we exemplify two...continue reading.
In a recent project at STATWORX, my team and I developed a large scale deep learning application for image classification using Keras and Tensorflow. After developing the model, we needed...continue reading.
In deep learning, there is no obvious way of obtaining uncertainty estimates. In 2016, Gal and Ghahramani proposed a method that is both theoretically grounded and practical: use dropout at...continue reading.
Object detection (the act of classifying and localizing multiple objects in a scene) is one of the more difficult, but very relevant in practice deep learning tasks. We’ll build up...continue reading.
Like GANs, variational autoencoders (VAEs) are often used to generate images. However, VAEs add an additional promise: namely, to model an underlying latent space. Here, we first look at a...continue reading.
STATWORX welcomes you to our open workshop “Deep Learning in R and Keras” ! The workshop is designed for advanced R-users who are fluent in importing, processing and manipulating data....continue reading.
Last Christmas is one of the most popular Christmas tunes that were, are and will be out there. The song is written by the brilliant musician George Michael and was...continue reading.
