Neural Networks

Neural networks are computing systems inspired by the biological neural networks in animal brains. They consist of interconnected nodes (neurons) organized in layers. The basic architecture includes an input layer, one or more hidden layers, and an output layer. Each connection has a weight that is adjusted during training. Activation functions introduce non-linearity, allowing neural networks to learn complex patterns. Training involves forward propagation (passing data through the network), calculating loss (difference between prediction and actual), and backpropagation (adjusting weights to minimize loss). Optimizers like SGD, Adam, and RMSProp control how weights are updated. Understanding neural networks provides the foundation for deep learning and enables solving complex problems that traditional machine learning algorithms struggle with.