NN (Neural Network)

Information

• Neural networks are a class of machine learning algorithms that are inspired by the structure and function of the human brain.
• They consist of layers of interconnected nodes, or artificial neurons, that can learn to recognize patterns in data by adjusting the weights of the connections between them.
• A typical neural network consists of an input layer, one or more hidden layers, and an output layer, with each layer consisting of multiple neurons.
• During training, the neural network is fed with input data, and the weights of the connections between neurons are adjusted in response to the errors produced by the network's predictions.
• Once trained, the neural network can be used to make predictions on new, unseen data.
• Neural networks have been applied to a wide range of problems, including image recognition, speech recognition, natural language processing, and more recently, to energy forecasting and load prediction.

Related Terminology

• DNN (Deep Neural Network)
• ELM (Extreme Learning Machine)
• FFNN (Feed Forward Neural Network)