AI deep learning

AI means getting a computer to mimic human behavior in some way.
Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.
Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.
Artificial intelligence as an academic discipline was founded in 1956. The goal then, as now, was to get computers to perform tasks regarded as uniquely human: things that required intelligence. The computer is doing something intelligent, so it’s exhibiting intelligence that is artificial. One category of techniques started becoming more widely used in the 1980s: machine learning.
Hard-coded algorithms or fixed, rule-based systems just didn’t work very well for things like image recognition or extracting meaning from text. The solution turned out to be not just mimicking human behavior (AI) but mimicking how humans learn.
Think about how you learned to read. You didn’t sit down and learn spelling and grammar before picking up your first book. You read simple books, graduating to more complex ones over time. You actually learned the rules (and exceptions) of spelling and grammar from your reading. Put another way, you processed a lot of data and learned from it.
That’s exactly the idea with machine learning. Feed an algorithm a lot of data and let it figure things out.
Put simply, deep learning is all about using neural networks with more neurons, layers, and interconnectivity. We’re still a long way off from mimicking the human brain in all its complexity, but we’re moving in that direction.
The field of artificial intelligence is essentially when machines can do tasks that typically require human intelligence. It encompasses machine learning, where machines can learn by experience and acquire skills without human involvement. Deep learning is a subset of machine...

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