Neural Networks

a type of machine learning algorithm inspired by the structure and function of the human brain.
They consist of multiple layers of interconnected nodes, or neurons, that process and transmit information.
Each neuron takes input data, performs a mathematical operation on it, and passes the result to the next layer of neurons.
During training, the network adjusts the strength of connections between neurons to optimize the output for a given input.
Neural networks can be used for a variety of tasks, such as image and speech recognition, natural language processing, and prediction.

 

Limitations of perceptrons:

There are cases where it is impossible to decide a linear boundary (ex. in XOR problems, the limit is 75% accuracy)

 

The Idea of MLP(MultiLayer Perceptron):

1. Hidden Layers

2. Sigmoid Functions

3. Back-Propogation Algorithms