R. Guha

Guha heads Corporate Business Development at Wipro Technologies and is focused on two strategic themes at the intersection of technology and business: cybersecurity framed from a business risk perspective and how to leverage machine learning for business transformation.

Guha is currently leading the development and deployment of Apollo, an anomaly detection platform that seeks to mitigate risk and improve process velocity through smarter detection.

Author's Posts

Four Key Things To Keep In Mind While Leveraging Machine Learning

Posted by: R. Guha | December 13, 2016

Technology is disrupting business processes across industries at an unprecedented and exponential scale, with several business domains now at the tipping point.

Demystifying Machine Learning Black Boxes For Greater Transparency – A Case For Building Causal Factor Prediction – Part II

Posted by: R. Guha | October 18, 2016

In my previous article, I spoke about how with the mainstreaming of Machine Learning and Deep Learning, it is becoming increasingly difficult to decipher how machines make decisions.

Demystifying machine learning black boxes for greater transparency - A case for building causal factor prediction - Part I

Posted by: R. Guha | October 18, 2016

In my previous article, I spoke about how with the mainstreaming of Machine Learning and Deep Learning, it is becoming increasingly difficult to decipher how machines make decisions.

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Author's Posts

Four Key Things To Keep In Mind While Leveraging Machine Learning

Demystifying Machine Learning Black Boxes For Greater Transparency – A Case For Building Causal Factor Prediction – Part II

Demystifying machine learning black boxes for greater transparency - A case for building causal factor prediction - Part I

Improved Anomaly Detection By Leveraging ‘Grey Labels’

How Does Machine Learning Improve Fraud Detection And Prevention?

How to protect your organization’s intellectual property without impacting business efficiency?

Do we really need powerful robots working the haystack

Challenges in Proactive Fraud Control

How do we build a genuinely effective early-warning system

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