Transfer Learning for Text using Deep Learning Virtual Machine (DLVM)

This post is by Anusua Trivedi, Data Scientist, and Wee Hyong Tok, Data Scientist Manager, at Microsoft. Motivation Modern machine learning models, especially deep neural networks, can often benefit quite significantly from transfer learning. In computer vision, deep convolutional neural networks trained on a large image classification datasets such as ImageNet have proved to be… Read more

Deep Learning for Emojis with VS Code Tools for AI

This post is the first in a two-part series, and is authored by Erika Menezes, Software Engineer at Microsoft. Visual content has always been a critical part of communication. Emojis are increasingly playing a crucial role in human dialogue conducted on leading social media and messaging platforms. Concise and fun to use, emojis can help improve… Read more

Deploying Deep Learning Models on Kubernetes with GPUs

This post is authored by Mathew Salvaris and Fidan Boylu Uz, Senior Data Scientists at Microsoft. One of the major challenges that data scientists often face is closing the gap between training a deep learning model and deploying it at production scale. Training of these models is a resource intensive task that requires a lot… Read more

Intelligent Edge: Building a Skin Cancer Prediction App with Azure Machine Learning, CoreML & Xamarin

This post is authored by Anusua Trivedi, Carlos Pessoa, Vivek Gupta & Wee Hyong Tok from the Cloud AI Platform team at Microsoft. Motivation AI has emerged as one of the most disruptive forces behind digital transformation and it is revolutionizing the way we live and work. AI-powered experiences are augmenting human capabilities and transforming… Read more

Training State-of-the-Art Neural Networks in the Microsoft Azure Cloud

This is the third post in a three-part series by guest blogger, Adrian Rosebrock. Adrian writes at PyImageSearch.com about computer vision and deep learning using Python. He recently finished authoring a new book on deep learning for computer vision and image recognition. Introduction In the final part in this series, I want to address a question I received from… Read more

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

Pixel-Level Land Cover Classification Using the Geo AI Data Science Virtual Machine and Batch AI

This post was authored by Mary Wahl, Kolya Malkin, Siyu Yang, Patrick Flickinger, Wee Hyong Tok, Lucas Joppa, and Nebojsa Jojic, representing the Microsoft Research and AI for Earth teams. Last week Microsoft launched the Geo AI Data Science Virtual Machine (DSVM), an Azure VM type specially tailored to data scientists and analysts that manage… Read more

ONNX Models to be Runnable Natively on 100s of Millions of Windows Devices

This post was authored by Eric Boyd, CVP, AI Data & Infrastructure. Today Microsoft is announcing the next major update to Windows will include the ability to run Open Neural Network Exchange (ONNX) models natively with hardware acceleration. This brings 100s of millions of Windows devices, ranging from IoT edge devices to HoloLens to 2-in-1s… Read more

Using Microsoft AI to Build a Lung-Disease Prediction Model Using Chest X-Ray Images

This blog post is co-authored by Xiaoyong Zhu, George Iordanescu and Ilia Karmanov, Data Scientists at Microsoft, and Mazen Zawaideh, Radiologist Resident at the University of Washington Medical Center. Introduction Artificial Intelligence (AI) has emerged as one of the most disruptive forces behind digital transformation that is revolutionizing the way we live and work. This… Read more

Image Data Support in Apache Spark

This post is co-authored by the Microsoft Azure Machine Learning team, in collaboration with Databricks Machine Learning team. Introduction Apache Spark is being increasingly used for deep learning applications for image processing and computer vision at scale. Problems such as image classification or object detection are being solved using deep learning frameworks such as Cognitive… Read more