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  2. Rectifier (neural networks) - Wikipedia

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

    Plot of the ReLU (blue) and GELU (green) functions near x = 0. In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function [1] [2] is an activation function defined as the non-negative part of its argument, i.e., the ramp function:

  3. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [ 24 ] PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo , a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and ...

  4. Activation function - Wikipedia

    en.wikipedia.org/wiki/Activation_function

    The activation function of a node in an artificial neural network is a function that calculates the output of the node based on its individual inputs and their weights. Nontrivial problems can be solved using only a few nodes if the activation function is nonlinear .

  5. Softplus - Wikipedia

    en.wikipedia.org/wiki/Softplus

    The convex conjugate (specifically, the Legendre transform) of the softplus function is the negative binary entropy (with base e).This is because (following the definition of the Legendre transform: the derivatives are inverse functions) the derivative of softplus is the logistic function, whose inverse function is the logit, which is the derivative of negative binary entropy.

  6. Swish function - Wikipedia

    en.wikipedia.org/wiki/Swish_function

    The swish paper was then updated to propose the activation with the learnable parameter β. In 2017, after performing analysis on ImageNet data, researchers from Google indicated that using this function as an activation function in artificial neural networks improves the performance, compared to ReLU and sigmoid functions. [ 1 ]

  7. Gated recurrent unit - Wikipedia

    en.wikipedia.org/wiki/Gated_recurrent_unit

    Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. [1] The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features, [2] but lacks a context vector or output gate, resulting in fewer parameters than LSTM. [3]

  8. Softmax function - Wikipedia

    en.wikipedia.org/wiki/Softmax_function

    In statistical mechanics, the softargmax function is known as the Boltzmann distribution (or Gibbs distribution): [5]: 7 the index set , …, are the microstates of the system; the inputs are the energies of that state; the denominator is known as the partition function, often denoted by Z; and the factor β is called the coldness (or ...

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