Category: Keras

In our overview of techniques for time-series forecasting, we move on to sequence-to-sequence models. Architectures in this family are commonly used in natural language processing (NLP) tasks, such as machine...continue reading.

We continue our exploration of time-series forecasting with torch, moving on to architectures designed for multi-step prediction. Here, we augment the “workhorse RNN” by a multi-layer perceptron (MLP) to extrapolate...continue reading.

This post is an introduction to time-series forecasting with torch. Central topics are data input, and practical usage of RNNs (GRUs/LSTMs). Upcoming posts will build on this, and introduce increasingly...continue reading.

Last month, we conducted our first survey on mlverse software, covering topics ranging from area of application through software usage to user wishes and suggestions. In addition, the survey asked...continue reading.

Today we introduce tabnet, a torch implementation of “TabNet: Attentive Interpretable Tabular Learning” that is fully integrated with the tidymodels framework. Per se, already, tabnet was designed to require very...continue reading.

This article translates Daniel Falbel’s post on “Simple Audio Classification” from TensorFlow/Keras to torch/torchaudio.continue reading.

El Niño-Southern Oscillation (ENSO) is an atmospheric phenomenon, located in the tropical Pacific, that greatly affects ecosystems as well as human well-being on a large portion of the globe. We...continue reading.

In forecasting spatially-determined phenomena (the weather, say, or the next frame in a movie), we want to model temporal evolution, ideally using recurrence relations. At the same time, we’d like...continue reading.

The torch 0.2.0 release includes many bug fixes and some nice new features like initial JIT support, multi-worker dataloaders, new optimizers and a new print method for nn_modules.continue reading.

Unlike all three previous sparklyr releases, the recent release of sparklyr 1.5 placed much more emphasis on enhancing existing sparklyr features rather than creating new ones. As a result, many...continue reading.

The need to segment images arises in various sciences and their applications, many of which are vital to human (and animal) life. In this introductory post, we train a U-Net...continue reading.

How not to die from poisonous mushrooms. Also: How to use torch for deep learning on tabular data, including a mix of categorical and numerical features.continue reading.

We learn about transfer learning, input pipelines, and learning rate schedulers, all while using torch to tell apart species of beautiful birds.continue reading.

We are excited to announce a number of powerful, new functionalities and improvements which are now part of sparklyr.flint 0.2!continue reading.

Today, we wrap up our mini-series on torch basics, adding to our toolset two abstractions: loss functions and optimizers.continue reading.

In this third installment of our mini-series introducing torch basics, we replace hand-coded matrix operations by modules, considerably simplifying our toy network’s code.continue reading.

With torch, there is hardly ever a reason to code backpropagation from scratch. Its automatic differentiation feature, called autograd, keeps track of operations that need their gradients computed, as well...continue reading.

In this first installment of a four-part miniseries, we present the main things you will want to know about torch tensors. As an illustrative example, we’ll code a simple neural...continue reading.

Sparklyr 1.4 is now available! This release comes with delightful new features such as weighted sampling and tidyr verbs support for Spark dataframes, robust scaler for standardizing data based on...continue reading.