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  2. Convolutional layer - Wikipedia

    en.wikipedia.org/wiki/Convolutional_layer

    In artificial neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers are some of the primary building blocks of convolutional neural networks (CNNs), a class of neural network most commonly applied to images, video, audio, and other data that have the property of uniform translational symmetry.

  3. Convolutional neural network - Wikipedia

    en.wikipedia.org/wiki/Convolutional_neural_network

    A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]

  4. Convolutional code - Wikipedia

    en.wikipedia.org/wiki/Convolutional_code

    In telecommunication, a convolutional code is a type of error-correcting code that generates parity symbols via the sliding application of a boolean polynomial function to a data stream. The sliding application represents the 'convolution' of the encoder over the data, which gives rise to the term 'convolutional coding'.

  5. AlexNet - Wikipedia

    en.wikipedia.org/wiki/AlexNet

    AlexNet contains eight layers: the first five are convolutional layers, some of them followed by max-pooling layers, and the last three are fully connected layers. The network, except the last layer, is split into two copies, each run on one GPU. [1] The entire structure can be written as

  6. Viterbi decoder - Wikipedia

    en.wikipedia.org/wiki/Viterbi_decoder

    A Viterbi decoder uses the Viterbi algorithm for decoding a bitstream that has been encoded using a convolutional code or trellis code. There are other algorithms for decoding a convolutionally encoded stream (for example, the Fano algorithm). The Viterbi algorithm is the most resource-consuming, but it does the maximum likelihood decoding. It ...

  7. LeNet - Wikipedia

    en.wikipedia.org/wiki/LeNet

    LeNet-4 was a larger version of LeNet-1 designed to fit the larger MNIST database. It had more feature maps in its convolutional layers, and had an additional layer of hidden units, fully connected to both the last convolutional layer and to the output units. It has 2 convolutions, 2 average poolings, and 2 fully connected layers.

  8. Joe Jonas Reveals the Prank That Led Him to be Airlifted to a ...

    www.aol.com/lifestyle/joe-jonas-reveals-prank...

    Jonas told the story via a popular TikTok format, which entails creating a slideshow of images of Pepe the King Prawn from The Muppets set to the tune of “Like a Prayer” from the Deadpool ...

  9. Universal approximation theorem - Wikipedia

    en.wikipedia.org/wiki/Universal_approximation...

    They showed that there exists an analytic sigmoidal activation function such that two hidden layer neural networks with bounded number of units in hidden layers are universal approximators. Guliyev and Ismailov [ 14 ] constructed a smooth sigmoidal activation function providing universal approximation property for two hidden layer feedforward ...