The advent of large language models (LLMs) marks a significant shift in how industries leverage AI to enhance operations and services. By automating routine...
Deep learning models require hundreds of gigabytes of data to generalize well on unseen samples. Data augmentation helps by increasing the variability of...
Mastering String Transformations in RAPIDS libcudf
Efficient processing of string data is vital for many data science applications. To extract valuable information from string data, RAPIDS libcudf provides...
Accelerating Medical Image Processing with NVIDIA DALI
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...
Accelerating Billion Vector Similarity Searches with GPUs
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,...
Gauss Rank Transformation Is 100x Faster with RAPIDS and CuPy
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...
Accelerating the Wide & Deep Model Workflow from 25 Hours to 10 Minutes Using NVIDIA GPUs
Recommender systems drive engagement on many of the most popular online platforms. As data volume grows exponentially, data scientists increasingly turn from...
Accelerating Inference with NVIDIA Triton Inference Server and NVIDIA DALI
When you are working on optimizing inference scenarios for the best performance, you may underestimate the effect of data preprocessing. These are the...
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...
