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  2. Unsupervised learning - Wikipedia

    en.wikipedia.org/wiki/Unsupervised_learning

    Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. [1] Other frameworks in the spectrum of supervisions include weak- or semi-supervision , where a small portion of the data is tagged, and self-supervision .

  3. Self-organizing map - Wikipedia

    en.wikipedia.org/wiki/Self-organizing_map

    The examples are usually administered several times as iterations. The training utilizes competitive learning. When a training example is fed to the network, its Euclidean distance to all weight vectors is computed. The neuron whose weight vector is most similar to the input is called the best matching unit (BMU). The weights of the BMU and ...

  4. Competitive learning - Wikipedia

    en.wikipedia.org/wiki/Competitive_learning

    Competitive learning is a form of unsupervised learning in artificial neural networks, in which nodes compete for the right to respond to a subset of the input data. [ 1 ] [ 2 ] A variant of Hebbian learning , competitive learning works by increasing the specialization of each node in the network.

  5. Hebbian theory - Wikipedia

    en.wikipedia.org/wiki/Hebbian_theory

    This is learning by epoch (weights updated after all the training examples are presented), being last term applicable to both discrete and continuous training sets. Again, in a Hopfield network, connections w i j {\displaystyle w_{ij}} are set to zero if i = j {\displaystyle i=j} (no reflexive connections).

  6. Deep belief network - Wikipedia

    en.wikipedia.org/wiki/Deep_belief_network

    The observation [2] that DBNs can be trained greedily, one layer at a time, led to one of the first effective deep learning algorithms. [4]: 6 Overall, there are many attractive implementations and uses of DBNs in real-life applications and scenarios (e.g., electroencephalography, [5] drug discovery [6] [7] [8]).

  7. Autoencoder - Wikipedia

    en.wikipedia.org/wiki/Autoencoder

    An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.

  8. Watchdog raises concerns over Trump-era leak probes of ... - AOL

    www.aol.com/watchdog-raises-concerns-over-trump...

    A top government watchdog raised concerns Tuesday over the handling of leak investigations during the first Trump administration that targeted members of Congress and the media despite finding no ...

  9. Active transport - Wikipedia

    en.wikipedia.org/wiki/Active_transport

    There are two types of active transport: primary active transport that uses adenosine triphosphate (ATP), and secondary active transport that uses an electrochemical gradient. This process is in contrast to passive transport , which allows molecules or ions to move down their concentration gradient, from an area of high concentration to an area ...

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