Comparing Deep Learning Frameworks: A Rosetta Stone Approach

This post is authored by Ilia Karmanov, Mathew Salvaris, Miguel Fierro, Danielle Dean, all Data Scientists at Microsoft. With this blog post, we are releasing a full version 1.0 of this repo, open-source on GitHub at: https://github.com/ilkarman/DeepLearningFrameworks. We believe deep-learning frameworks are like languages: Sure, many people speak English, but each language serves its own… Read more

Deep Learning with Caffe2 on the Azure Data Science Virtual Machine

This post is authored by Gopi Kumar, Principal Program Manager, and Paul Shealy, Senior Software Engineer, at Microsoft. With the availability of ultra-fast GPUs (Graphics Processing Units), compute-intensive deep learning algorithms are becoming increasingly popular. Deep learning algorithms are particularly versatile at deriving insights from large amounts of information across rich formats such as text,… Read more

Deep Learning on the New Ubuntu-Based Data Science Virtual Machine for Linux

Authored by Paul Shealy, Senior Software Engineer, and Gopi Kumar, Principal Program Manager, at Microsoft. Deep learning has received significant attention recently for its ability to create machine learning models with very high accuracy. It’s especially popular in image and speech recognition tasks, where the availability of massive datasets with rich information make it feasible… Read more