Neural networks are a class of machine learning models inspired by the structure and function of the human brain. They consist of interconnected nodes, or “neurons,” organized into layers. These layers include an input layer, one or more hidden layers, and an output layer.
Each neuron in a neural network receives input data, performs a computation on it using weights and biases, and then passes the result to the next layer. The weights and biases are the parameters that the network learns during the training process.
The basic building block of a neural network is the perceptron, which takes multiple inputs, applies weights to them, sums them up, adds a bias term, and then passes the result through an activation function. The activation function introduces non-linearity into the model, allowing neural networks to capture complex patterns in data.
Training a neural network involves a two-step process: forward propagation and backpropagation. During forward propagation, input data is passed through the network, and predictions are generated. The difference between these predictions and the actual target values is measured using a loss function. Backpropagation is then used to adjust the weights and biases in a way that minimizes the loss, often utilizing optimization algorithms like gradient descent.
Neural networks have various architectures, including feedforward neural networks, convolutional neural networks (CNNs) for image processing, and recurrent neural networks (RNNs) for sequential data. More advanced architectures like Long Short-Term Memory (LSTM) networks and Transformers have been developed to handle specific tasks, such as natural language processing and language translation.