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A good reference gaining a basic understanding of deep networks from an ML perspective (at least the forms commonly used for computer vision tasks) is Stanford's course CS231n: Convolutional Neural Networks for Visual Recognition. It's a fantastic introduction and highly recommended.


This reminds me of another good example in the field of ML which is the perceptron algorithm. If you're interested, look at notes from 15-451


@Calloc I am familiar with the perceptron algorithm from 451, but I do not see the overlap between perceptron and deep learning.


@ote Each unit in a neural net is a linear combination of the inputs(Perceptron-ish) with some non-linear transformation to produce the output. A neural network is just a network of these units.


@ote A common tool in machine learning is the multilayer perceptron which is essentially a deep neural network. You can learn more about it here: