Neural networks are computational models inspired by the structure and function of the human brain. They consist of interconnected nodes, called neurons, that work together to process information. Just like neurons in our brain communicate through synapses, artificial neurons are connected and pass signals to each other. This brain-inspired architecture allows neural networks to learn patterns, recognize images, understand language, and make complex decisions.
The artificial neuron is the fundamental building block of neural networks. Each neuron receives multiple inputs, represented as x1, x2, and x3. These inputs are multiplied by corresponding weights w1, w2, and w3, which determine the importance of each input. The neuron then sums all these weighted inputs together, adds a bias term, and passes the result through an activation function. This activation function decides whether the neuron should fire and send a signal to the next layer.
Neural networks are organized into layers that process information sequentially. The input layer receives the raw data, such as pixel values from an image or features from a dataset. Hidden layers perform the actual computation and feature extraction, transforming the input data through weighted connections and activation functions. Finally, the output layer produces the network's prediction or classification result. Information flows forward through these layers, with each neuron receiving inputs from the previous layer and sending outputs to the next layer.
Neural networks learn through a process called training. During the forward pass, input data flows through the network layers to produce an output. This output is then compared with the desired target to calculate an error. Through backpropagation, the network adjusts its weights and biases to minimize this error. This process repeats thousands of times, gradually improving the network's performance. As training progresses, the error decreases and the network becomes better at making accurate predictions on new, unseen data.