enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  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

    The convolution has stride 1, zero-padding, with kernel size 3-by-3. The convolution kernel is a discrete Laplacian operator. The convolutional layer is the core building block of a CNN. The layer's parameters consist of a set of learnable filters (or kernels), which have a small receptive field, but extend through the full depth of the input ...

  4. 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.

  5. Inception (deep learning architecture) - Wikipedia

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

    As an example, a single 5×5 convolution can be factored into 3×3 stacked on top of another 3×3. Both has a receptive field of size 5×5. The 5×5 convolution kernel has 25 parameters, compared to just 18 in the factorized version. Thus, the 5×5 convolution is strictly more powerful than the factorized version.

  6. Residual neural network - Wikipedia

    en.wikipedia.org/wiki/Residual_neural_network

    A bottleneck block [1] consists of three sequential convolutional layers and a residual connection. The first layer in this block is a 1x1 convolution for dimension reduction (e.g., to 1/2 of the input dimension); the second layer performs a 3x3 convolution; the last layer is another 1x1 convolution for dimension restoration.

  7. Video of military police officer throwing man off bridge ...

    www.aol.com/video-military-police-officer...

    The video, which was shared widely on social media and the Brazilian press, showed an officer grabbing a civilian by the shirt and walking him towards the side of a bridge. Moments later, the ...

  8. Patrick Duffy Says the “Dallas ”Cast Would Start Every ...

    www.aol.com/lifestyle/patrick-duffy-says-dallas...

    Related: The Original Cast of 'Dallas': Where Are They Now? Duffy remembered that the day the original cast met for the first time, they gathered at Warner Bros. studios. "And then Haggy came in ...

  9. Layer (deep learning) - Wikipedia

    en.wikipedia.org/wiki/Layer_(Deep_Learning)

    The Recurrent layer is used for text processing with a memory function. Similar to the Convolutional layer, the output of recurrent layers are usually fed into a fully-connected layer for further processing. See also: RNN model. [6] [7] [8] The Normalization layer adjusts the output data from previous layers to achieve a regular distribution ...