It is a powerful learning algorithm inspired by how the brain works.
What is neural network?
Example 1 – single neural network
Given data about the size of houses on the real estate market and you want to fit a function that will
predict their price. It is a linear regression problem because the price as a function of size is a continuous
output.
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We know the prices can never be negative so we are creating a function called Rectified Linear Unit (ReLU)
which starts at zero.
The input is the size of the house (x)
The output is the price (y)
The “neuron” implements the function ReLU (blue line)
Example 2 – Multiple neural network
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The price of a house can be affected by other features such as size, number of bedrooms, zip code and
wealth. The role of the neural network is to predicted the price and it will automatically generate the
hidden units. We only need to give the inputs x and the output y.
Input layer
Hidden unit