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deep learning

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    • durée

      1 min de lecture (153 words)

    • source

      llms.txt

    See also: The Little Book of Deep Learning (pdf or lectures), annotated history of deep learning, this lecture series at CMU

    deep learning

    Imagine you’re learning to recognize dogs. At first, your parents point to different dogs and say “that’s a dog!” After seeing lots of dogs, you start noticing patterns - they have four legs, fur, tails, and make barking sounds. Now you can spot dogs on your own!

    Deep learning works kind of like that, but for computers. The computer looks at tons of pictures (like thousands and thousands), and slowly figures out what makes a dog look like a dog. It starts with simple things like edges and shapes, then builds up to more complicated stuff like spotting ears, tails, and finally whole dogs.

    Deep learning is a superset of Machine learning. Supervised and un-supervised refers to the training objective within machine learning, rather than the goal of different algorithms.

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