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INFO: Full form for LAC is Local Analysis Center in Computing category
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INFO: Full form for LAC is Loop Assignment Center in Computing category
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INFO: Full form for LAC is Lan Card in Computing category
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INFO: Full form for LAC is License Authorization Code in Computing category
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INFO: Full form for LAC is Lsp Aggregation Candidate in Computing category
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INFO: Full form for LAC is Linux Audio Conference in Computing category
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INFO: Full form for JTA is Junction Tree Algorithm in Computing category
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INFO: Full form for JTA is Java transaction API in Computing category
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INFO: Full form for ESPN is Entertainment And Sprots Programming Network in Computing category
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INFO: Full form for JFC is Java Fundamental Classes in Computing category
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INFO: Full form for CNN is Catbox News Network in Computing category
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INFO: Full form for CNN is Created News Netwotk in Computing category
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A container is any receptacle or enclosure for holding a product used in storage, packaging, and transportation, including shipping.Things kept inside of a container are protected by being inside of its structure. The term is most frequently applied to devices made from materials that are durable and are often partly or completely rigid.

A container can also be considered as a basic tool, consisting of any device creating a partially or fully enclosed space that can be used to contain, store, and transport objects or materials.

INFO: Full form for CNN is Composite Network Node in Computing category
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In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network, most commonly applied to analyze visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are only equivariant, as opposed to invariant, to translation. They have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain-computer interfaces, and financial time series.

CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The "full connectivity" of these networks make them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include: penalizing parameters during training (such as weight decay) or trimming connectivity (skipped connections, dropout, etc.) CNNs take a different approach towards regularization: they take advantage of the hierarchical pattern in data and assemble patterns of increasing complexity using smaller and simpler patterns embossed in their filters. Therefore, on a scale of connectivity and complexity, CNNs are on the lower extreme.

Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different neurons partially overlap such that they cover the entire visual field.

CNNs use relatively little pre-processing compared to other image classification algorithms. This means that the network learns to optimize the filters (or kernels) through automated learning, whereas in traditional algorithms these filters are hand-engineered. This independence from prior knowledge and human intervention in feature extraction is a major advantage.

INFO: Full form for CNN is Cellular Neural Network in Computing category
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INFO: Full form for HBD is Hard Bit Decision in Computing category
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INFO: Full form for FEB is Functionals Electronic Block in Computing category
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