How to bake a Data Science pie?
Surbhi Anand (~surbhi53) |
Data Science problems are being bought in to solve a plethora of problems in each and every industry. And as the complexity of these problems increases, we need more and more time to solve them. But, we don’t really have that luxury in corporate life. The only escape here is to make use of what’s already available. In this talk, we will focus on this idealogy of reusing existing techniques by decomposing an existing complex Machine Learning problem.
With the number of open sourced Machine Learning algorithms, it’s highly unlikely to not find what you are looking for, in one form or another. These other conceptually similar problems, which aren’t directly usable, can be used by atomizing complex problems into simpler forms which then become trivial to solve.
Here, we take the problem of breaking down a video into semantic units and then walk the audience through the thought process and approach that we took to write minimal Machine Learning code in this entire process.
The talk is meant for anyone making an attempt at a Machine Learning Problem. We don’t expect any pre-requisite knowledge. The audience should be able to walk away with a structured way of approaching a complex machine learning problem. The main objective of the talk is to throw light on the approach of ensembling and fast iterations for a data science problem.
Shavak Agrawal : I am a Data Scientist at Microsoft and have previously worked at Flipkart (A Walmart Company) and IBM Research. While at each of these organizations, I have given talks and presentations to various stakeholders to explain a variety of Machine Learning problems. I have also previously conducted multiple courses in college on Machine Learning and Databases.
Surbhi Anand : I have been working with Bing for solving complex data science problems affecting millions of users. I have presented a talk on Query-Title similarity using Siamese architecture at Synapse 2018, Microsoft’s Annual data science conference.I have delivered presentations and talks while working at Microsoft for around 2 years now.