Neural networks, inspired by the human brain’s structure, are a fundamental element in machine learning and artificial intelligence. At a basic level, a neural network consists of interconnected nodes, or “neurons,” organized into layers: the input layer, one or more hidden layers, and the output layer. Each connection between neurons has an associated weight, which gets adjusted during training. When data is fed into the network, it passes through these layers. Neurons process the data by multiplying the input values by their weights, summing the results, and then applying an activation function to produce the neuron’s output. As the data flows from layer to layer, the network learns to make increasingly complex decisions. The true power of neural networks emerges during the training phase. Using a dataset, the network makes predictions, and the errors between its predictions and the actual values are measured. An optimization algorithm, like gradient descent, then adjusts the weights to minimize this error. Over multiple iterations, the network refines its weights and biases, enhancing its accuracy and ability to make predictions or classify data.