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  2. Layer (deep learning) - Wikipedia

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

    The Pooling layer [5] is used to reduce the size of data input. 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 ...

  3. VGA text mode - Wikipedia

    en.wikipedia.org/wiki/VGA_text_mode

    All glyphs on screen are the same size, but that size is variable. Typically, glyphs are 8 dots wide and 8–16 dots high, however the height can be any value up to a maximum of 32. Each row of a glyph is coded in an 8-bit byte, with high bits to the left of the glyph and low bits to the right.

  4. Boolean operations on polygons - Wikipedia

    en.wikipedia.org/wiki/Boolean_operations_on_polygons

    David Kennison's Polypack, a FORTRAN library based on the Vatti algorithm. Klamer Schutte's Clippoly, a polygon clipper written in C++. Michael Leonov's poly_Boolean, a C++ library, which extends the Schutte algorithm. Angus Johnson's Clipper, an open-source freeware library (written in Delphi, C++ and C#) that's based on the Vatti algorithm.

  5. Residual neural network - Wikipedia

    en.wikipedia.org/wiki/Residual_neural_network

    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. The models of ResNet-50, ResNet-101, and ResNet-152 are all based on bottleneck blocks. [1]

  6. Transformer (deep learning architecture) - Wikipedia

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

    The number of neurons in the middle layer is called intermediate size (GPT), [56] filter size (BERT), [36] or feedforward size (BERT). [36] It is typically larger than the embedding size. For example, in both GPT-2 series and BERT series, the intermediate size of a model is 4 times its embedding size: d ffn = 4 d emb {\displaystyle d_{\text{ffn ...

  7. U-Net - Wikipedia

    en.wikipedia.org/wiki/U-Net

    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. Segmentation of a 512 × 512 image takes less than a second on a modern (2015) GPU using the U-Net architecture.

  8. Text mode - Wikipedia

    en.wikipedia.org/wiki/Text_mode

    Text mode is a computer display mode in which content is internally represented on a computer screen in terms of characters rather than individual pixels.Typically, the screen consists of a uniform rectangular grid of character cells, each of which contains one of the characters of a character set; at the same time, contrasted to graphics mode or other kinds of computer graphics modes.

  9. Graphics pipeline - Wikipedia

    en.wikipedia.org/wiki/Graphics_pipeline

    The computer graphics pipeline, also known as the rendering pipeline, or graphics pipeline, is a framework within computer graphics that outlines the necessary procedures for transforming a three-dimensional (3D) scene into a two-dimensional (2D) representation on a screen. [1]