Drilling Down into Machine Learning and Deep Learning Posted on : May 23 - 2017

This is the year of artificial intelligence and deep learning, when the technology is coming into its own for mainstream businesses. AI-based tools are pouring into the marketplace, but machine learning has been around for decades. So, why does this matter? In short, because we need to gain insights from massive amounts of data, and this process requires systems that exceed human capabilities. Machine learning algorithms can dig through mountains of data to ferret patterns that might not otherwise be recognizable. Moreover, machine learning algorithms get better over time, because they learn from their experiences.

Machine learning is a subclass of AI techniques which automate the learning process through algorithms and high-powered data analytics. In recent years, advances in data science, combined with significant increases and declining cost of computing power, have yielded swelling data lakes and increasingly sophisticated analytics that have, for all practical purposes, made machine learning and AI business-ready.

Deep learning in turn, is subclass of machine learning that creates machines that use methods originally inspired by how a cat’s brain reacted with light signals and then generalized to mimic the human brain’s ability to learn. Until recently, we simply didn’t have enough data and proces- sing power to train a machine to learn. Deep neural networks (DNNs) learn at many levels of abstraction, ranging from simple concepts to complex ones. This is what designates the “deep” in deep learning. Each layer in the neural network categorizes some kind of information, refines it, and passes it along to the next layer. Deep learning lets the machine use this progression to build a hierarchical representation. View More