Deep Learning with R, 2nd Edition
Announcing the release of “Deep Learning with R, 2nd Edition,” a book that shows you how to get started with deep learning in R.
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Using torch modules
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.
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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