What is Neural Network ?

 What is a Neural Network?

Neural networks are machine learning models that mimic the complex functions of the human brain. These models consist of interconnected nodes or neurons that process data, learn patterns and enable tasks such as pattern recognition and decision-making.

Understanding Neural Networks in Deep Learning

Neural networks are capable of learning and identifying patterns directly from data without pre-defined rules. These networks are built from several key components:

• Neurons: The basic units that receive inputs, each neuron is governed by a threshold and an activation function.
• Connections: Links between neurons that carry information, regulated by weights and biases.
• Weights and Biases: These parameters determine the strength and influence of connections.
• Propagation Functions: Mechanisms that help process and transfer data across layers of neurons.
• Learning Rule: The method that adjusts weights and biases over time to improve accuracy.

How do neural networks work?

The human brain is the inspiration behind neural network architecture. Human brain cells, called neurons, form a complex, highly interconnected network and send electrical signals to each other to help humans process information. Similarly, an artificial neural network is made of artificial neurons that work together to solve a problem. Artificial neurons are software modules, called nodes, and artificial neural networks are software programs or algorithms that, at their core, use computing systems to solve mathematical calculations.