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  2. Glossary of artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Glossary_of_artificial...

    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.

  3. Neural network (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Neural_network_(machine...

    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] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain. Artificial ...

  4. Neural network - Wikipedia

    en.wikipedia.org/wiki/Neural_network

    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.

  5. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a mapping of the set of words to a vector space , typically of several hundred dimensions , with each unique word in the corpus being assigned a vector in the space.

  6. Activation function - Wikipedia

    en.wikipedia.org/wiki/Activation_function

    Folding activation functions are extensively used in the pooling layers in convolutional neural networks, and in output layers of multiclass classification networks. These activations perform aggregation over the inputs, such as taking the mean, minimum or maximum. In multiclass classification the softmax activation is often used.

  7. Word embedding - Wikipedia

    en.wikipedia.org/wiki/Word_embedding

    In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]

  8. Generative artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Generative_artificial...

    The new generative models introduced during this period allowed for large neural networks to be trained using unsupervised learning or semi-supervised learning, rather than the supervised learning typical of discriminative models. Unsupervised learning removed the need for humans to manually label data, allowing for larger networks to be ...

  9. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    Fully recurrent neural networks (FRNN) connect the outputs of all neurons to the inputs of all neurons. In other words, it is a fully connected network. This is the most general neural network topology, because all other topologies can be represented by setting some connection weights to zero to simulate the lack of connections between those ...