Another field of research that has grown increasingly popular in ML is deep learning. It is a supervised learning technique but has unsupervised learning features. They are useful particularly for tasks where an observation or basic unit has little meaning in and of itself but a collection of these units or certain combination has very useful meaning. These come from its ability to learn from the data that is passed to it. It is inspired by the structure of a brain much like neural network, where each node or neuron has a set of inputs with specific weights and computes some function (such as linear but can be anything we want really). The network is created when we start connecting neurons to each other, the input data and the outlets (where we store the analysed information, the "answer" to our problem). Deep networks have a specific number of neurons in each layer where each subsequent layer is able to learn the best possible representation for data from the previous one. Take the example below of a deep neural network for facial recognition:
The figure on the right shows what a simple neural network
would look like. We have the input nodes(or neutrons), a hidden layer of neurons that process the data, and finally the output nodes that essentially give us a some sort of solution.
Image Reference:
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzman
Writing Reference:
http://www.kdnuggets.com/2015/01/deep-learning-explanation-what-how-why.html
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