
Articles

Question Answering Models: A Comparison
This article covers a deeper level understanding of Question Answering models in NLP, the datasets commonly used, and how to choose a pre-trained model by considering various factors like the document structure, runtime cost, etc.
Concurrent Spatial and Channel Squeeze & Excitation (scSE) Nets
In this article, we will take a look at the paper "Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks" which serves as a design update for the popular Squeeze-and-Excitation attention module.
Shuffle Attention for Deep Convolutional Neural Networks (SA-Net)
This article gives an in-depth summary of the ICASSP paper titled "SA-Net: Shuffle Attention for Deep Convolutional Neural Networks."
Comparison: SageMaker Studio Notebooks and Paperspace Gradient Notebooks
AWS SageMaker Studio Notebooks are feature-rich and yet present a number of difficulties when getting started and when trying to understand pricing. Here we take a look at the favorable and unfavorable comparisons with Gradient Notebooks from Paperspace.
Comparison: Google's AI Platform Notebooks and Paperspace's Gradient Notebooks
Google AI Platform Notebooks are enterprise-grade notebooks best suited for those with compliance concerns who need to ingest data from GCP sources like BigQuery. Gradient is more like Google's other notebook product Colab but with advanced features.