A neural network is a computational model inspired by the human brain. It consists of interconnected artificial neurons organized in layers. The input layer receives data, hidden layers process information, and the output layer produces results. These networks can learn patterns from data to make predictions and solve complex problems.
Each artificial neuron works like a simple processing unit. It receives multiple inputs, each multiplied by a weight. These weighted inputs are summed together, and a bias value is added. The result is then passed through an activation function, which determines the neuron's output. This mathematical operation allows the neuron to learn and make decisions based on the input data.
Forward propagation is how data flows through a neural network. Input data enters the input layer and is processed by each subsequent layer. The information moves forward through hidden layers, where each neuron applies weights, biases, and activation functions. Finally, the output layer produces a prediction. This forward flow creates the network's response to the input data.
Neural networks learn through backpropagation. After making a prediction, the network compares its output to the expected result and calculates the error. This error is then propagated backward through the network, adjusting weights and biases to reduce future errors. Through many iterations of this process, the network gradually improves its performance and learns to make better predictions.
To summarize how neural networks work: They process information through interconnected layers of artificial neurons. Each neuron applies mathematical operations including weights, biases, and activation functions. Data flows forward through the network to make predictions, while backpropagation adjusts the network's parameters to learn from errors and improve performance over time.