Fundamentals of Neural Networks and multilayer perceptron model.pptx

Fundamentals of neural networks

1. Neural Networks

2. Artificial Neural Network ANN’s learn relationship between cause and effect or organizing large volumes of data into orderly and informative pattern. Characteristics of Brain Parallel Computing Learning is based only on local information Learning ability and generalization Learning is constant and usually unsupervised Connects get reorganized based on experience Performance degrades if some units are removed Characteristics of ANN(desired) Massive Parallel processing Robust

3. Perceptron Model Learns from Experience Strength of connection between the neurons is stored as a weight value for the specific connection. Characterization 1. Architecture- A pattern of connection between neurons Single Layer Feedforward Multi-layer feedforward Recurrent 2. Strategy –Learning Algorithm Supervised Unsupervised Reinforced 3. Activation Function-Function to compute output Linear Non-linear

4. Introduction

5. Multilayer Perceptron