A Gentle Introduction to Neural Networks
At their core, neural networks consist of layers of interconnected nodes that learn to approximate complex functions. Each layer transforms its inputs through weights and activation functions, gradually building richer representations.
1. Layers and Activations
A typical network starts with an input layer, followed by one or more hidden layers, and ends with an output layer. Activation functions like ReLU, sigmoid, or tanh introduce non-linearity, enabling the network to model complicated relationships.
2. Training via Backpropagation
During training, the network makes predictions and measures how far they deviate from the true labels. The backpropagation algorithm computes gradients of the error with respect to each weight, allowing an optimizer such as gradient descent to adjust the network toward better performance.
Neural networks underpin everything from image recognition to natural language processing. Understanding their basic mechanics is the first step toward exploring the broader world of deep learning.
