Structure of Neural Networks

Neural Networks Overview

Artificial neural networks (ANNs) are the computational foundation of machine learning models. They are inspired by the structure and functioning of biological neural networks in the human brain. ANNs consist of interconnected layers of nodes or neurons, each of which processes input data and generates an output.

Components of Neural Networks

• Input Layer: Accepts raw data inputs for processing.

• Hidden Layers: Consist of multiple layers between the input and output layers. These layers process inputs using mathematical transformations, extracting patterns and features.

• Output Layer: Produces the final prediction or classification.

Types of Neural Networks

• Feed-Forward Neural Networks: Data flows in one direction—from input to output—through hidden layers.

• Recurrent Neural Networks (RNNs): Include loops to process sequences of data, enabling tasks like natural language processing.

Training Neural Networks

• Training Data: Neural networks are trained using cleaned and well-defined datasets.

• Optimization: The weights of the connections between nodes are adjusted to minimize prediction errors.

• Activation Functions: Functions like sigmoid or ReLU determine the output of each node by applying transformations.

Deep Learning

Deep learning is a subset of machine learning that employs complex, multilayer (deep) neural networks. These networks can solve sophisticated problems such as image recognition and language translation by using multiple hidden layers.

Practical Applications

While deep neural networks can include hundreds of layers for complex problems, many business applications are effectively solved with two or three hidden layers. Modern training algorithms make these networks practical and scalable for real-world applications​​.