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  2. Residual neural network - Wikipedia

    en.wikipedia.org/wiki/Residual_neural_network

    Originally, ResNet was designed for computer vision. [1] [8] [9] The Transformer architecture includes residual connections. All transformer architectures include residual connections. Indeed, very deep transformers cannot be trained without them. [10] The original ResNet paper made no claim on being inspired by biological systems.

  3. Highway network - Wikipedia

    en.wikipedia.org/wiki/Highway_network

    In machine learning, the Highway Network was the first working very deep feedforward neural network with hundreds of layers, much deeper than previous neural networks. [1] [2] [3] It uses skip connections modulated by learned gating mechanisms to regulate information flow, inspired by long short-term memory (LSTM) recurrent neural networks.

  4. Inception (deep learning architecture) - Wikipedia

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

    Inception [1] is a family of convolutional neural network (CNN) for computer vision, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1).). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern

  5. Air gap (networking) - Wikipedia

    en.wikipedia.org/wiki/Air_gap_(networking)

    An air gapped network (right) with no connection to a nearby internet-connected network (left) An air gap, air wall, air gapping [1] or disconnected network is a network security measure employed on one or more computers to ensure that a secure computer network is physically isolated from unsecured networks, such as the public Internet or an unsecured local area network. [2]

  6. Batch normalization - Wikipedia

    en.wikipedia.org/wiki/Batch_normalization

    In a neural network, batch normalization is achieved through a normalization step that fixes the means and variances of each layer's inputs. Ideally, the normalization would be conducted over the entire training set, but to use this step jointly with stochastic optimization methods, it is impractical to use the global information.

  7. Heterogeneous network - Wikipedia

    en.wikipedia.org/wiki/Heterogeneous_network

    In computer networking, a heterogeneous network is a network connecting computers and other devices where the operating systems and protocols have significant differences. For example, local area networks (LANs) that connect Windows, Linux and Macintosh computers are heterogeneous.

  8. HTTP persistent connection - Wikipedia

    en.wikipedia.org/wiki/HTTP_persistent_connection

    Under HTTP 1.0, connections should always be closed by the server after sending the response. [1]Since at least late 1995, [2] developers of popular products (browsers, web servers, etc.) using HTTP/1.0, started to add an unofficial extension (to the protocol) named "keep-alive" in order to allow the reuse of a connection for multiple requests/responses.

  9. AlexNet - Wikipedia

    en.wikipedia.org/wiki/AlexNet

    For computer vision in particular, much progress came from manual feature engineering, such as SIFT features, SURF features, HoG features, bags of visual words, etc. It was a minority position in computer vision that features can be learned directly from data, a position which became dominant after AlexNet.