Neural networks are a type of machine learning algorithm modeled after the structure and function of the human brain. They are used to solve a variety of tasks, such as classification, regression, and clustering.
A neural network is composed of multiple layers of interconnected neurons. The first layer is the input layer, which receives the input data. The last layer is the output layer, which produces the output. The layers in between are called hidden layers. Each neuron in a layer is connected to every neuron in the next layer, and each connection has a weight associated with it.
Neural networks are trained using a process called backpropagation. During training, the network is fed a set of input data and the corresponding output data. The network then produces an output, and the difference between the output and the expected output is calculated using a loss function. The weights of the connections are adjusted using gradient descent to minimize the loss function. This process is repeated for multiple epochs until the network reaches an acceptable level of accuracy.
There are several types of neural networks, including:
Neural networks have many applications in various fields, including: