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Machine Learning vs. Deep Learning: In Apps and Business Posted on Jan 11 - 2018

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Machine learning vs. deep learning isn’t exactly a boxing knockout – deep learning is a subset of machine learning, and both are subsets of artificial intelligence (AI). However, there is a lot of confusion in the marketplace around the definitions and use cases of machine learning and deep learning, so let’s clear up the confusion.

·  Artificial intelligence (AI) is the study of simulating and imitating intelligent human behavior in computer systems and machines.

·  Machine learning is a sub-domain of AI, and uses algorithms to apply AI concepts to computing systems. Computers identify and act upon data patterns, and over time learn to improve their accuracy without explicit programming. Machine learning is behind analytics like predictive coding, clustering, and visual heat maps.

·  Deep learning is a sub-domain of machine learning and is another name for artificial neural networks. Deep learning computer networks simulate the way a human brain perceives, organizes, and makes decisions from data input. Skynet aside, deep learning exists today.

Machine Learning vs. Deep Learning

In truth, the idea of machine learning vs. deep learning misses the point – as mentioned, deep learning is a subset of machine learning. At this point, you are much more likely to employ machine learning in your applications than deep learning, which is still a developing technology and expensive to deploy. But some offerings are already in the market, and over time deep learning will become more common.

Let’s look at the distinctions and usage cases between the two. 

Machine Learning

As a subset of AI, machine learning uses algorithms to parse data, learn from the results, and apply the learning to make decisions or predictions. Examples include clustering, Bayesian networks, and visual data mapping. In eDiscovery and compliance investigations for example, heat maps and visual clusters can present graphic search results to humans, who can use the results to drill down into otherwise obscure data.

Machine learning technology falls into two types: supervised machine learning and unsupervised machine learning. Supervised learning depends on a human-generated seed set that teaches the software how it should define data. Predictive coding is a prime example. The software refers to the seed set to match data patterns to a relevancy percentage. Over time the predictive coding tool learns from ongoing reviewer feedback. View More


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