Also known as: nn · Neural networks · ANN
Un ANN (Artificial Neural Network), ou réseau neuronal artificiel, est un modèle d’apprentissage automatique composé d’unités computationnelles interconnectées, souvent organisées en couches, qui transforment des entrées en sorties au moyen de poids appris et de fonctions d’activation. Les réseaux neuronaux peuvent apprendre des motifs complexes à partir des données et sont utilisés pour la classification, la régression, la perception, le traitement du langage et la génération. Les réseaux neuronaux profonds comportent plusieurs couches cachées et sont centraux dans les systèmes d’IA modernes.
A computer structure inspired by the biological brain, consisting of a large set of interconnected computational units ('neurons') that are connected in layers. Data passes between these units as between neurons in a brain. Outputs of a previous layer are used as inputs for the next, and there can be hundreds of layers of units. An artificial neural network with more than 3 layers is considered a deep learning algorithm. Examples of artificial neural networks include Transformers or Generative adversarial networks.
A neural network can refer to either a neural circuit of biological neurons (sometimes also called a biological neural network), a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activatio
A model containing at least one hidden layer. A deep neural network is a type of neural network containing more than one hidden layer. For example, the following diagram shows a deep neural network containing two hidden layers. !A neural network with an input layer, two hidden layers, and an output layer. Each neuron in a neural network connects to all of the nodes in the next layer. For example, in the preceding diagram, notice that each of the three neurons in the first hidden layer separately connect to both of the two neurons in the second hidden layer. Neural networks implemented on computers are sometimes called artificial neural networks to differentiate them from neural networks found in brains and other nervous systems. Some neural networks can mimic extremely complex nonlinear relationships between different features and the label. See also convolutional neural network and recurrent neural network. See Neural networks in Machine Learning Crash Course for more information.