Input layer definition
Input layer refers to the first layer of nodes in an artificial neural network. This layer receives input data from the outside world. An artificial neural network (ANN) is a machine learning model inspired by the structure and function of the human brain. The input layer consists of artificial neurons and is responsible for transforming raw input data into a format the network can understand.
See also: node, artificial intelligence
How the input layer works in artificial neural networks
- The input layer plays an important role in artificial neural networks because it allows them to receive and make sense of information.
- The input layer consists of one or more artificial neurons.
- Each neuron receives input from the outside world (e.g., image, text, video).
- The input layer passes this information on to the next layer in the artificial neural network (typically a hidden layer).
- The neurons in the hidden layer process the information, then pass the results on to the next layer of artificial neurons.
- The processing of the input data can be adjusted during the training phase of the artificial neural network. The artificial neural network (ANN) adapts over time, improving its ability to make more accurate predictions.
