Introduction to Neural Networks

Neural networks are a fundamental concept in the field of artificial intelligence and machine learning. They are inspired by the human brain's interconnected neurons and are used to model complex patterns and relationships in data.

At its core, a neural network consists of layers of interconnected nodes, known as neurons, which process and transform input data. Each connection between neurons has associated weights that are adjusted during training to optimize the network's performance.

How Neural Networks Work

Neural networks work through a process called forward and backward propagation. In the forward pass, input data is passed through the network's layers, and computations are performed using the weights and activation functions. The output of the network is compared to the desired output, and an error is calculated.

In the backward pass, the network adjusts the weights using optimization algorithms such as gradient descent. This process aims to minimize the error by updating the weights in a way that reduces the difference between predicted and actual outputs.

Applications of Neural Networks

Neural networks have found applications in various domains, including image and speech recognition, natural language processing, recommendation systems, and more. Convolutional Neural Networks (CNNs) are commonly used for image analysis, while Recurrent Neural Networks (RNNs) are suitable for sequence data.