Grasping how neural nets work

If research and advisory firm Gartner Inc. is right in its forecast, Artificial Intelligence (AI) technologies will become pervasive in almost every new software product and service by the year 2020. The growth in AI, broadly a set of computational technologies and methodologies aimed at helping machines emulate human intelligence, is being driven primarily by sophisticated algorithms, the availability of huge data sets, greater computing power, and advances in machine learning as well as deep learning. Machine learning, a subset of AI, is broadly about teaching a computer how to spot patterns and use mountains of data to make connections without any programming to accomplish the specific task. A recommendation engine is a good example. Deep learning, an advanced machine learning technique, uses layered (hence “deep”) neural networks (neural nets) that are loosely modelled on the human brain. Neural nets enable image recognition, speech recognition, self-driving cars and smarthome automation devices, among other things. More From Livemint » A neural net comprises thousands or even millions of simple processing nodes that are densely interconnected . An individual node might be connected to several nodes in the layer beneath it, from which it receives data, and several nodes in the layer…

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