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A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models. While individual neurons are simple, many of them together in a network can perform complex tasks. There are two main types of neural network.
Above: An image classifier, an example of a neural network trained with a discriminative objective. Below: A text-to-image model, an example of a network trained with a generative objective. Since its inception, the field of machine learning used both discriminative models and generative models, to model and predict data.
Microsoft Cognitive Toolkit (previously known as CNTK), an open source toolkit for building artificial neural networks. [82] OpenNN, a comprehensive C++ library implementing neural networks. [83] PyTorch, an open-source Tensor and Dynamic neural network in Python. [84] TensorFlow, an open-source software library for machine learning. [85]
Capsule neural network; Catastrophic interference; Cellular neural network; Cerebellar model articulation controller; CoDi; Committee machine; Competitive learning; Compositional pattern-producing network; Computational cybernetics; Computational neurogenetic modeling; Confabulation (neural networks) Connectionist temporal classification ...
When the activation function is non-linear, then a two-layer neural network can be proven to be a universal function approximator. [6] This is known as the Universal Approximation Theorem . The identity activation function does not satisfy this property.
Classical examples include word embeddings and autoencoders. [10] [11] Self-supervised learning has since been applied to many modalities through the use of deep neural network architectures such as convolutional neural networks and transformers. [9]
A kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s. [7] [8] Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework.
In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [1] [2] A neural network consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain ...