Posts

Convolutional LSTM for spatial forecasting

In forecasting spatially-determined phenomena (the weather, say, or the next fra...

torch 0.2.0 - Initial JIT support and many bug fixes

The torch 0.2.0 release includes many bug fixes and some nice new features like ...

sparklyr 1.5: better dplyr interface, more sdf_* functi...

Unlike all three previous sparklyr releases, the recent release of sparklyr 1.5 ...

Brain image segmentation with torch

The need to segment images arises in various sciences and their applications, ma...

torch for tabular data

How not to die from poisonous mushrooms. Also: How to use torch for deep learnin...

Classifying images with torch

We learn about transfer learning, input pipelines, and learning rate schedulers,...

sparklyr.flint 0.2: ASOF Joins, OLS Regression, and add...

We are excited to announce a number of powerful, new functionalities and improve...

Optimizers in torch

Today, we wrap up our mini-series on torch basics, adding to our toolset two abs...

Using torch modules

In this third installment of our mini-series introducing torch basics, we replac...

Introducing torch autograd

With torch, there is hardly ever a reason to code backpropagation from scratch. ...

Getting familiar with torch tensors

In this first installment of a four-part miniseries, we present the main things ...

sparklyr 1.4: Weighted Sampling, Tidyr Verbs, Robust Sc...

Sparklyr 1.4 is now available! This release comes with delightful new features s...

Please allow me to introduce myself: Torch for R

Today, we are excited to introduce torch, an R package that allows you to use Py...

Introducing sparklyr.flint: A time-series extension for...

We are pleased to announce that sparklyr.flint, a sparklyr extension for analyzi...

An introduction to weather forecasting with deep learning

A few weeks ago, we showed how to forecast chaotic dynamical systems with deep l...

Training ImageNet with R

This post explores how to train large datasets with TensorFlow and R. Specifical...

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