Artificial Intelligence

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sparklyr 1.4: Weighted Sampling, Tidyr Verbs, Robust Scaler, RAPIDS, and more

Artificial Intelligence, Distributed Computing, Packages/Releases, R

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 median and interquartile range, spark_connect interface for RAPIDS GPU acceleration plugin, as well as a number of dplyr-related improvements.

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Introductory time-series forecasting with torch

Artificial Intelligence, R, Time Series, Torch

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 involved architectures.

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Getting familiar with torch tensors

Artificial Intelligence, R, Torch

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 network from scratch.

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torch time series continued: A first go at multi-step prediction

Artificial Intelligence, R, Time Series, Torch

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 multiple timesteps into the future.

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Introducing torch autograd

Artificial Intelligence, R, Torch

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 as how to compute them. In this second post of a four-part series, we update our simple, hand-coded network to make use of autograd.

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torch time series, take three: Sequence-to-sequence prediction

Artificial Intelligence, R, Time Series, Torch

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 translation. With NLP, however, significant pre-processing is required before proceeding to model definition and training. In staying with our familiar numerical series, we can fully

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Using torch modules

Artificial Intelligence, R, Torch

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.

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