A Primer On Deep LearningA Primer On Deep Learning – The term “artificial intelligence” (AI) has been around since the 1950s, but there has long existed a yawning gap between what people thought AI to be and what was actually possible. Since the 1960s, much of what has been considered to be AI has been a form of machine learning. However, despite the leaps and bounds in technology since the term was first introduced, the accomplishments of machine learning have often fallen short of public expectations (think the robots in The Jetsons).

Fortunately, after a half-century of research, that gap between our expectations and reality is finally closing, thanks to deep learning, a more advanced type of machine learning that’s capable of generating human-like insight. Here, I will give an introduction to deep learning and explore the potential of this groundbreaking new field.

Before we dive into deep learning, we need to first define what machine learning is. Machine learning basically involves giving data to an algorithm and then “training” it to perform a certain task, such as giving out movie recommendations based on a user’s past watching history. In other words, with machine learning, you plug the data in and get an answer in return. This is distinct from the older approach of having a programmer write a program to generate movie recommendations. Machine learning uses the data to write the program.

 So, what is deep learning? Deep learning takes machine learning to the next level through the use of artificial neural networks. These networks are essentially layers of artificial “neurons” in a rough approximation of the way that human brains work. These neural networks operate by taking an input — say, an image of a cat — and passing it through layers and layers of neurons that each perform simple computations until ultimately the last layer of neurons output the name of the object in the image: “cat.”
This article originally appeared in forbes.com.  To read the full article, click here.
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