A neural network is a type of Machine Learning algorithm modeled after the structure and function of the human brain. It is composed of many interconnected units called "neurons" which are organized into multiple layers. The layers of a neural network can be thought of as different stages of processing. Data is input into the network at the input layer, which then passes through one or more hidden layers before reaching the output layer.
At each layer, the neurons process the input data using a mathematical function and pass the result on to the next layer. The output of the network is determined by the weights, or strengths, of the connections between the neurons. These weights are adjusted during the training process in order to improve the accuracy of the output.
Neural networks are used for a variety of tasks such as image and speech recognition, Natural Language Processing, and even playing games. In these tasks, the neural network is trained on a large dataset and then used to make predictions or decisions based on new input data. For example, a neural network trained on a dataset of images of animals might be able to classify new images as belonging to one of several different animal classes.
One of the key advantages of neural networks is their ability to learn and improve over time. By adjusting the weights of the connections between neurons, a neural network can adapt to new data and make more accurate predictions. This allows them to be used in a wide range of applications where the desired output is not known in advance.
In summary, a neural network is a type of machine learning algorithm that is composed of interconnected neurons organized into layers. It is trained on a dataset and used to make predictions or decisions based on new input data. Its ability to learn and adapt makes it useful for a variety of tasks.