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  2. Residual neural network - Wikipedia

    en.wikipedia.org/wiki/Residual_neural_network

    A basic block is the simplest building block studied in the original ResNet. [1] This block consists of two sequential 3x3 convolutional layers and a residual connection. The input and output dimensions of both layers are equal. Block diagram of ResNet (2015). It shows a ResNet block with and without the 1x1 convolution.

  3. Leela Zero - Wikipedia

    en.wikipedia.org/wiki/Leela_Zero

    The body is a ResNet with 40 residual blocks and 256 channels. There are two heads, a policy head and a value head. Policy head outputs a logit array of size 19 × 19 + 1 {\displaystyle 19\times 19+1} , representing the logit of making a move in one of the points, plus the logit of passing .

  4. AlphaGo Zero - Wikipedia

    en.wikipedia.org/wiki/AlphaGo_Zero

    8 channels are the positions of the other player's stones from the last eight time steps. 1 channel is all 1 if black is to move, and 0 otherwise. The body is a ResNet with either 20 or 40 residual blocks and 256 channels. There are two heads, a policy head and a value head.

  5. Neural network (machine learning) - Wikipedia

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

    Choice of model: This depends on the data representation and the application. Model parameters include the number, type, and connectedness of network layers, as well as the size of each and the connection type (full, pooling, etc. ). Overly complex models learn slowly. Learning algorithm: Numerous trade-offs exist between learning algorithms.

  6. 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]

  7. Inception (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Inception_(deep_learning...

    Inception v2 was released in 2015, in a paper that is more famous for proposing batch normalization. [7] [8] It had 13.6 million parameters.It improves on Inception v1 by adding batch normalization, and removing dropout and local response normalization which they found became unnecessary when batch normalization is used.

  8. Vanishing gradient problem - Wikipedia

    en.wikipedia.org/wiki/Vanishing_gradient_problem

    Residual connections, or skip connections, refers to the architectural motif of +, where is an arbitrary neural network module. This gives the gradient of ∇ f + I {\displaystyle \nabla f+I} , where the identity matrix do not suffer from the vanishing or exploding gradient.

  9. Straight-eight engine - Wikipedia

    en.wikipedia.org/wiki/Straight-eight_engine

    Dual overhead camshaft Duesenberg Model J engine. Italy's Isotta Fraschini introduced the first production automobile straight-eight in their Tipo 8 at the Paris Salon in 1919 [3] Leyland Motors introduced their OHC straight-eight powered Leyland Eight luxury car at the International Motor Exhibition at Olympia, London in 1920.