data preprocessing

Deep learning models require hundreds of gigabytes of data to generalize well on unseen samples. Data augmentation helps by increasing the variability of...

Efficient processing of string data is vital for many data science applications. To extract valuable information from string data, RAPIDS libcudf provides...

Deep learning models require vast amounts of data to produce accurate predictions, and this need becomes more acute every day as models grow in size and...

This post is an update to an older post. Deep learning models require training with vast amounts of data to achieve accurate results. Raw data usually cannot be...

Relying on the capabilities of GPUs, a team from Facebook AI Research has developed a faster, more efficient way for AI to run similarity searches. The study,...

As explained in the Batch Normalization paper, training neural networks becomes way easier if its input is Gaussian. This is clear. And if your model inputs are...

Recommender systems drive engagement on many of the most popular online platforms. As data volume grows exponentially, data scientists increasingly turn from...

When you are working on optimizing inference scenarios for the best performance, you may underestimate the effect of data preprocessing. These are the...

Recently, NVIDIA CEO Jensen Huang announced updates to the open beta of NVIDIA Merlin, an end-to-end framework that democratizes the development of large-scale...

Editor's Note: This post has been updated. Here is the revised post. Training deep learning models with vast amounts of data is necessary to achieve accurate...