ON MEASURING MACHINE LEARNING MODELS AGAINST CONCRETE BUSINESS OBJECTIVES

REVIEW NOTES: DATA SCIENCE FOR BUSINESS BY PROVOST & FAWCETT: CHAPTER 7 I enjoyed reading this chapter. It's insightful and well explained with detailed examples, diagrams and graphics, on a few data science topics that correspond directly to conventional scientific research in computer science. That makes me happy, because these are crucial points, yet rarely … Continue reading ON MEASURING MACHINE LEARNING MODELS AGAINST CONCRETE BUSINESS OBJECTIVES

Collaboration Platforms for Data Scientists

News from April 10th 2019 is the release of Google's collaborative AI platform for Data Science teams, for execution on cloud or on premises. Google's platform joins Alibaba's similar platform called PAI 2.0 announced in March 29th 2017. While comprehensive information on Alibaba's platform is sparse in non-Chinese, the Google AI Platform does give samples … Continue reading Collaboration Platforms for Data Scientists

How to capture structure from relational data?

Here's an interesting article just published by Matthew Das Sarma, Stanford University on May 1st 2018, for extracting relational structure from data without structure. Here the researcher(s) discuss the approach and benefits of random walks through unstructured data to find relationships between data nodes. The walks through real-world graphs are reported to observe a power-law … Continue reading How to capture structure from relational data?