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Posted on July 26, 2016 at 5:00am 2 Comments 1 Like
Overview
There are huge numbers of variants of deep architectures as it’s a fast developing field and so it helps to mention other leading algorithms. The list is intended to be comprehensive but not exhaustive since so many algorithms are being developed [1] [2][1],[2].
Posted on July 20, 2016 at 5:00am 3 Comments 1 Like
While Deep Learning has shown itself to be very powerful in applications, the underlying theory and mathematics behind it remains obscure and vague. Deep Learning works, but theoretically we do not understand much why it works. Some leading machine learning theorists like Vladimir Vapnik criticise Deep Learning for its ad-hoc approach that gives a strong flavour of brute force rather than technical sophistication. Deep Learning is not theory intensive; it is empirical based more (hence…
ContinuePosted on June 26, 2016 at 5:00am 0 Comments 1 Like
This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of loss adjusters, social media comments, claim documents and review of medical doctors etc. Unstructured data has massive potential but has never been traditionally considered as a source of insight before.…
ContinuePosted on June 20, 2016 at 2:30am 0 Comments 0 Likes
Overview
“The only function of economic forecasting is to make astrology look respectable” John Kenneth Galbraith
Predictive modeling and traditional ratemaking is an exercise of forecasting the future, whether directly or indirectly (indirectly as generalizing historical lessons to the future). But is such forecasting so hopeless as being same as seeing into a crystal ball?
The advantage of data scientist and actuaries is their close in contact with…
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