Any one know how to build neural network..???
Answers
A neural network is a collection of “neurons” with “synapses” connecting them. The collection is organized into three main parts: the input layer, the hidden layer, and the output layer. Note that you can have n hidden layers, with the term “deep” learning implying multiple hidden layers.
Screenshot taken from this great introductory video, which trains a neural network to predict a test score based on hours spent studying and sleeping the night before.
Hidden layers are necessary when the neural network has to make sense of something really complicated, contextual, or non obvious, like image recognition. The term “deep” learning came from having many hidden layers. These layers are known as “hidden”, since they are not visible as a network output. Read more about hidden layers here and here.
The circles represent neurons and lines represent synapses. Synapses take the input and multiply it by a “weight” (the “strength” of the input in determining the output). Neurons add the outputs from all synapses and apply an activation function.
Training a neural network basically means calibrating all of the “weights” by repeating two key steps, forward propagation and back propagation.
Since neural networks are great for regression, the best input data are numbers (as opposed to discrete values, like colors or movie genres, whose data is better for statistical classification models). The output data will be a number within a range like 0 and 1 (this ultimately depends on the activation function—more on this below).
In forward propagation, we apply a set of weights to the input data and calculate an output. For the first forward propagation, the set of weights is selected randomly.
In back propagation, we measure the margin of error of the output and adjust the weights accordingly to decrease the error.
Neural networks repeat both forward and back propagation until the weights are calibrated to accurately predict an output.