Neural Network is a concept which tries to mimic human brain to solve computational issues. Neural network consists of basic units called Neurons. Each neuron has some functions and they together with other neurons solve complex issues. Neural network as a concept has been around since 1960’s (maybe before that too). But it lost its importance in 1990’s. Due to recent advancements in computational power and some algorithmic breakthrough’s, it has started to play an important role for many complex machine learning problems.
Neuron : Each Neuron can have one or more inputs and one or more outputs. Each connection between other neurons with some weights associated.
Feed forward : The input is processed through one or many layers of neurons to get the output.
Back Propagation : The Error which is the difference between the predicted output and the original target output, is propagated from the output to the input in a reverse manner, so that the weights of the neurons adjust itself to the error.
Full code :
Learning materials :
- https://dl.dropboxusercontent.com/u/7412214/BackPropagation.pdf (Coding was inspired by Ryan)