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.
Artificial Intelligence
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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.
Parallelized sampling using exponential variates
How can the seemingly iterative process of weighted sampling without replacement be transformed into something highly parallelizable? Turns out a well-known technique based on exponential variates accomplishes exactly that.
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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.
State-of-the-art NLP models from R
Nowadays, Microsoft, Google, Facebook, and OpenAI are sharing lots of state-of-the-art models in the field of Natural Language Processing. However, fewer materials exist how to use these models from R. In this post, we will show how R users can access and benefit from these models as well.
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.
Differential Privacy with TensorFlow
Differential Privacy guarantees that results of a database query are basically independent of the presence in the data of a single individual. Applied to machine learning, we expect that no single training example influences the parameters of the trained model in a substantial way. This post introduces TensorFlow Privacy, a library built on top of