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  2. Keras - Wikipedia

    en.wikipedia.org/wiki/Keras

    Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into the TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers ...

  3. Zahn's construct - Wikipedia

    en.wikipedia.org/wiki/Zahn's_construct

    Zahn's construct in computer science, also known as the "situation case statement", was a proposed structure for structured control flow in computer programming languages first described by Charles T. Zahn in 1974. [1]

  4. Dilution (neural networks) - Wikipedia

    en.wikipedia.org/wiki/Dilution_(neural_networks)

    On the left is a fully connected neural network with two hidden layers. On the right is the same network after applying dropout. Dilution and dropout (also called DropConnect [1]) are regularization techniques for reducing overfitting in artificial neural networks by preventing complex co-adaptations on training data.

  5. Stochastic gradient descent - Wikipedia

    en.wikipedia.org/wiki/Stochastic_gradient_descent

    There, () is the value of the loss function at -th example, and () is the empirical risk. When used to minimize the above function, a standard (or "batch") gradient descent method would perform the following iterations: w := w − η ∇ Q ( w ) = w − η n ∑ i = 1 n ∇ Q i ( w ) . {\displaystyle w:=w-\eta \,\nabla Q(w)=w-{\frac {\eta }{n ...

  6. Mixture of experts - Wikipedia

    en.wikipedia.org/wiki/Mixture_of_experts

    In words, the experts that, in hindsight, seemed like the good experts to consult, are asked to learn on the example. The experts that, in hindsight, were not, are left alone. The combined effect is that the experts become specialized: Suppose two experts are both good at predicting a certain kind of input, but one is slightly better, then the ...

  7. Time delay neural network - Wikipedia

    en.wikipedia.org/wiki/Time_delay_neural_network

    In the case of a speech signal, inputs are spectral coefficients over time. In order to learn critical acoustic-phonetic features (for example formant transitions, bursts, frication, etc.) without first requiring precise localization, the TDNN is trained time-shift-invariantly. Time-shift invariance is achieved through weight sharing across time d

  8. Seq2seq - Wikipedia

    en.wikipedia.org/wiki/Seq2seq

    Shannon's diagram of a general communications system, showing the process by which a message sent becomes the message received (possibly corrupted by noise). seq2seq is an approach to machine translation (or more generally, sequence transduction) with roots in information theory, where communication is understood as an encode-transmit-decode process, and machine translation can be studied as a ...

  9. NP-hardness - Wikipedia

    en.wikipedia.org/wiki/NP-hardness

    A simple example of an NP-hard problem is the subset sum problem. Informally, if H is NP-hard, then it is at least as difficult to solve as the problems in NP. However, the opposite direction is not true: some problems are undecidable, and therefore even more difficult to solve than all problems in NP, but they are probably not NP-hard (unless ...