Deepcric is a deep learning system for cricket. It looks at cricket video and does scene segmentation, scene classification, automatic commentary generation, targeted highlights generation, player identification, and player stats extraction.

Deep learning has been applied everywhere. From imagenet [1] to disease identification [2] to large-scale video classification [3] to text classification [4], there are barely any areas where people have not applied deep learning. But interestingly, there has been very little work in applying data science and deep learning to the game of cricket. This post is a detailed overview of my final year project at the FAST National University. We have developed a deep learning based system that is able to do many tasks in cricket in an automated way. Some of these tasks are:

In a few seconds flat, you’ve gone from a neatly-equipped office to a home cinema…all within the same four walls. Who needs more than one room?

This is the dream of those who work on “programmable matter.”

In his recent book about AI, Max Tegmark makes a distinction between three different levels of computational sophistication for organisms. Life 1.0 is single-celled organisms like bacteria; here, hardware is indistinguishable from software. The behavior of the bacteria is encoded into its DNA; it cannot learn new things.