Artificial Neural Networks (Advantages (Adaptive learning, Self…
Artificial Neural Networks
Biologically inspired simulations performed on the computer to perform certain specific tasks like clustering, classification, pattern recognition etc.
The first artificial neuron was produced in 1943 by neuro physiologist Warren McCulloch and logic Walter Pits
In 1951, Marvin Minsky created the first ANN while working at Princeton.
The Mark I Perceptron was also created in 1958, at Cornell University by Frank Rosenblatt
A Neural Network can be trained to classify given pattern or data set into predefined class. It uses Feedforward Networks.
A Neural Network can be trained to produce outputs that are expected from given input. E.g., - Stock market prediction.
The Neural network can be used to identify a unique feature of the data and classify them into different categories without any prior knowledge of the data.
A Neural Network can be trained to remember the particular pattern, so that when the noise pattern is presented to the network..
Connections and weights
Contains Artificial Neurons which receive input from the outside world on which network will learn, recognize about or otherwise process.
It contains units that respond to the information about how it's learned any task.
These units are in between input and output layers. The job of hidden layer is to transform the input into something that output unit can use in some way.
Gradient Descent Algorithm
Back Propagation Algorithm
Self - Organizing Kohonen Rule
LMS algorithm (Least Mean Square)