Neural Networks, which turns out to be a much better way to learn complex hypotheses, complex nonlinear hypotheses even when your input feature space, even when n is large.
Neurons and the Brain
Neural Networks are a pretty old algorithm that was originally motivated by the goal of having machines that can mimic the brain.
It’s actually a very effective state of the art technique for modern day machine learning applications.
A neuron is is a computational unit that gets a number of inputs through its input wires, does some computation, and then it sends outputs, via its axon to other nodes or other neurons in the brain.
Weights of a model just means exactly the same thing as parameters of the model.
Forward Propagation : Because it start of with the activation of the input-units and then it sort of forward-propagate that to the hidden layer and then it sort of forward propagate that and compute the activation of the output layer.
The more complex features will be better than x^n, and it will be more work well for prediction new data.
Examples and Intuitions
In normal Logistic Regression, though we can use some polynomial to contract some features, we still be limited by original features.But in Neuron Network, the original features just be work on input layer.
We can use contract neuron network to be more complex neuron network that will do more complex compute.
four output units represents four classification