Deep Machine Learning often referred to as deep learning by the media is the mimicking human brain’s neo cortex by an AI engine. This sub-branch of machine learning is very nascent, largely deriving from neural network research of 1980s and some representational breakthroughs in 2006. Deep learning offers some solutions to problems such as reading hand-writing or finding objects in images using machines.
Several software toolkits such as opencv, mlib, tensorflow, thenos provide a set of neural network representations and algorithms for Deep Machine Learning. Middleware like keras makes it easy to enable toolkit portability.
There are several industry problems, which are currently using deep learning - the top problem areas are around image search, and machine vision (automotive/ aviation).
At this stage, we are very interested in image search and application of automated learning to find data schemas for health care, finance and other domains and are exploring use cases to begin testing.
Deep Learning literature overview
- Brian Hur