What Developers Need to Consider When Exploring Machine Learning
This is the first article in a five-part series that covers the steps to take before launching a machine learning startup. The complete report, available here, covers how to get started, choose a framework, decide what applications and technology to use, and more.
While artificial intelligence (AI), machine learning and deep learning are often thought of as being interchangeable, they do in fact relate to very different concepts. It all began in the 1950s with AI and the idea that a computer could be made to simulate human learning and intelligence.
A subclass of that is machine learning, whereby a computer can take large amounts of data and use it begin to recognize patterns, make predictions on new data, and essentially ‘learn’ for itself. The drawback is that machine learning requires that parameters be set for what the computer needs to recognize, and those inputs can be time-consuming. And so we go one step further, into deep learning.
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