- Over the past few years, the term “deep learning” has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics.
- Deep Learning focuses even more narrowly on a subset of ML tools and techniques, and applies them to solving just about any problem which requires “thought” – human or artificial.
- Essentially Deep Learning involves feeding a computer system a lot of data, which it can use to make decisions about other data.
- Because Deep Learning work is focused on developing these networks, they become what are known as Deep Neural Networks – logic networks of the complexity needed to deal with classifying datasets as large as, say, Google’s image library, or Twitter’s firehose of tweets.
- But Deep Learning can be applied to any form of data – machine signals, audio, video, speech, written words – to produce conclusions that seem as if they have been arrived at by humans – very, very fast ones.
Deep Learning is not only a massive buzzword spanning business and technology but also a concept that will transform most industries and jobs, as well as the way we live our lives. However, there is confusion about what it is and how it differs from Machine Learning and Artificial Intelligence (AI)
@Forbes: What’s the difference between deep learning, machine learning and AI? Here’s a handy guide:
Quote of the Day
The intuitive mind is a sacred gift and the rational mind is a faithful servant.