Neural Networks
Neural networks are a class of machine learning algorithms that are modeled after the structure and function of the human brain. They are composed of layers of interconnected nodes, called neurons, that receive input from other neurons and produce an output signal. Each neuron is connected to one or more other neurons through weights, which represent the strength of the connection between neurons.
The input to a neural network consists of a set of features or variables, which are fed into the input layer of the network. The input layer then passes the information to one or more hidden layers, where the data is transformed through a series of mathematical operations. The final output is generated by the output layer, which represents the prediction or classification made by the network.
The key to the success of neural networks is their ability to learn from data. During the training process, the weights of the connections between neurons are adjusted to minimize the error between the predicted output and the actual output. This process, known as backpropagation, involves propagating the error back through the network and adjusting the weights accordingly.
There are many different types of neural networks, each with its own unique architecture and application. For example, convolutional neural networks are commonly used for image recognition tasks, while recurrent neural networks are used for sequential data processing tasks, such as natural language processing.