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.

Calloc

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 http://www.cs.cmu.edu/~15451/lectures/lec17.pdf.

ote

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

mperron

@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.

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 http://www.cs.cmu.edu/~15451/lectures/lec17.pdf.

@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: https://en.wikipedia.org/wiki/Multilayer_perceptron