A neural network is a computational model inspired by the structure and function of the human brain. It's designed to recognize patterns and make decisions, just like our brains do.
Neural networks consist of layers of interconnected nodes called neurons. Each connection has a weight that determines its strength. The basic structure includes an input layer, one or more hidden layers, and an output layer.
Each neuron receives inputs, processes them with an activation function, and passes the output to the next layer. The activation function determines whether the neuron should fire. In this example, we see inputs x1, x2, and x3 with corresponding weights w1, w2, and w3, processed by an activation function to produce output y.
Activation functions introduce non-linearity into the network, allowing it to learn complex patterns. Common types include the Sigmoid function, which produces a smooth S-shaped curve; ReLU, which returns the maximum of zero and the input value; and Tanh, which is the hyperbolic tangent function.
Neural networks learn through training. First, forward propagation occurs where input data flows through the network. Then, loss calculation compares the output with the expected result. Backpropagation adjusts the weights to minimize error. This process repeats until the network performs well on the task.
Neural networks have diverse applications. They're used in image recognition like facial recognition, natural language processing for translation, medical diagnosis, financial forecasting, autonomous vehicles, and game playing such as chess and Go.