Artificial Neural Networks (ANNs) learn relationships between cause and effect by organizing data into meaningful patterns, resembling the characteristics of the human brain. They exhibit features such as parallel processing, the ability to learn from experiences, and the use of weights to strengthen connections between neurons. The architecture of ANNs varies, with types including single-layer feedforward, multi-layer feedforward, and recurrent, while learning strategies can be supervised, unsupervised, or reinforced.

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

7. Multilayer Perceptron