Readers’ Choice – Our 10 Most Popular ML Blog Posts of 2014
We launched this blog in June 2014 with the intent of sharing important advances and practical knowledge accumulated by Microsoft in the field of ML. After six months of regular posts, many of them authored by world-leading ML researchers and practitioners, we are seeing tens of thousands of readers such as yourself regularly visiting our blog site where, we hope, you are finding articles of value and relevance to your own ML journeys.
As we take one final look back at the year 2014, we figured we would share the top 10 most-read posts of 2014. Here they are, listed below, in increasing order of popularity.
10. Machine Learning, meet Computer Vision
Jamie Shotton, Antonio Criminisi and Sebastian Nowozin explore the challenges of computer vision and touch on the powerful ML technique of decision forests for pixel-wise classification.
9. Python Tools for Visual Studio now integrates with Azure Machine Learning
Shahrokh Mortazavi talks about Python support in Azure ML, including the powerful Python centric Data Science IDE, PTVS – a completely free and open source tool that is helping democratize ML and advanced analytics.
8. Vowpal Wabbit for Fast Learning John Langford shares information about the speedy VW open source ML system sponsored by Microsoft.
7. Machine Learning and Text Analytics Dr. Ashok Chandra talks about how we are now able to take advantage of signals to determine the salient entities being discussed in textual articles.
6. The Joy (and Hard Work) of Machine Learning
Joseph Sirosh discusses how enterprises can tap into the potential of ML to deliver enormous value in diverse applications that can improve customer experience, reduce the risk of systemic failures, grow revenue and bring about significant cost savings.
5. Machine Learning Trends from NIPS 2014
John Platt shares 3 exciting trends he saw at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal this year.
4. What is Machine Learning?
John Platt provides some much-needed context around ML and also shares a taxonomy of ML applications.
3. Twenty Years of Machine Learning at Microsoft
John Platt discusses Microsoft’s 20+ years of experience in creating ML systems and applying them to real world problems, including what it takes to actually deploy ML in production.
2. How Azure ML Partners are Innovating for their Customers
At the Worldwide Partner Conference, Joseph Sirosh talks about how Azure ML – which is changing the game for building ML applications at scale and in the cloud – is being used by Microsoft’s partners to rapidly build novel solutions for our customers.
1. Rapid Progress in Automatic Image Captioning
John Platt talks about the exciting progress researchers have made in creating systems to automatically generate descriptive captions of images.
We wish our readers a very happy and productive 2015!
ML Blog Team