Optimizers in torch
Today, we wrap up our mini-series on torch basics, adding to our toolset two abstractions: loss functions and optimizers.
sparklyr.flint 0.2: ASOF Joins, OLS Regression, and additional summarizers
We are excited to announce a number of powerful, new functionalities and improvements which are now part of sparklyr.flint 0.2!
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Classifying images with torch
We learn about transfer learning, input pipelines, and learning rate schedulers, all while using torch to tell apart species of beautiful birds.
torch for tabular data
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.
Brain image segmentation with torch
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 to mark lesioned regions on MRI brain scans.
sparklyr 1.5: better dplyr interface, more sdf_* functions, and RDS-based serialization routines
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 valuable suggestions from sparklyr users were taken into account and were successfully addressed in a long list of bug fixes and improvements.
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 initial JIT support, multi-worker dataloaders, new optimizers and a new print method for nn_modules.
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Convolutional LSTM for spatial forecasting
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 to efficiently extract spatial features, something that is normally done with convolutional filters. Ideally then, we’d have at our disposal an architecture that is both
Forecasting El Niño-Southern Oscillation (ENSO)
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 use the convLSTM introduced in a prior post to predict the Niño 3.4 Index from spatially-ordered sequences of sea surface temperatures.
Simple audio classification with torch
This article translates Daniel Falbel’s post on “Simple Audio Classification” from TensorFlow/Keras to torch/torchaudio.