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Prince Grover

Prince Grover
Prince Grover is a Machine Learning Engineer at Manifold. His previous work includes machine learning and deep learning for problems in the computer vision, oil and gas, and real estate domains. He holds an MS degree in Analytics & Data Science from the University of San Francisco, as well as MS & BS degrees in Geophysical Technology from IIT Roorkee.
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Recent Posts

Custom Loss Functions for Gradient Boosting

Posted by Prince Grover on Sep 28, 2018 3:27:51 PM

By Prince Grover and Sourav Dey

 

Gradient boosting is widely used in industry and has won many Kaggle competitions. The internet already has many good explanations of gradient boosting (we've even shared some selected links in the references), but we've noticed a lack of information about custom loss functions: the why, when, and how. This post is our attempt to summarize the importance of custom loss functions in many real-world problems — and how to implement them with the LightGBM gradient boosting package.

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Topics: Data science, Data engineering

Applications of Matrix Decompositions for Machine Learning

Posted by Prince Grover on Jul 25, 2018 9:00:00 AM

In machine learning and statistics, we often have to deal with structural data, which is generally represented as a table of rows and columns, or a matrix. A lot of problems in machine learning can be solved using matrix algebra and vector calculus. In this blog, I’m going to discuss a few problems that can be solved using matrix decomposition techniques. I’m also going to talk about which particular decomposition techniques have been shown to work better for a number of ML problems.

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Topics: Data science

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