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The architecture of vision transformer. An input image is divided into patches, each of which is linearly mapped through a patch embedding layer, before entering a standard Transformer encoder. A vision transformer (ViT) is a transformer designed for computer vision. [1]
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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
A fully connected layer for an image of size 100 × 100 has 10,000 weights for each neuron in the second layer. Convolution reduces the number of free parameters, allowing the network to be deeper. [6] For example, using a 5 × 5 tiling region, each with the same shared weights, requires only 25 neurons.
U-Net is a convolutional neural network that was developed for image segmentation. [1] The network is based on a fully convolutional neural network [2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation.
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raksyBH / Getty Images Although they’re often drawn to vibrant cities for their career opportunities and lifestyle perks, high housing costs make living in these urban hubs increasingly difficult.
A two-layer neural network capable of calculating XOR. The numbers within the neurons represent each neuron's explicit threshold. The numbers that annotate arrows represent the weight of the inputs. Note that If the threshold of 2 is met then a value of 1 is used for the weight multiplication to the next layer.