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\nFundamentals of Neural Networks - Convolutional Operation\n

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\nThe Convolution layer (CONV) uses filters that perform convolution operations as it is scanning the input with respect to its dimensions. Its hyperparameters include the filter size and stride. The resulting output is called a feature...\nThe Convolution layer (CONV) uses filters that perform convolution operations as it is scanning the input with respect to its dimensions. Its hyperparameters include the filter size and stride. The resulting output is called a feature map or activation map.

This clip is from the chapter \"Convolutional Neural Networks\" of the series \"Fundamentals in Neural Networks\".This section explains convolutional neural networks where you will start with image data. You will also see some operations that can allow us to take advantage of image data that neural networks originally failed to do so.\n
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11:39
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Grade
Higher Ed
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Math
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\nPackt - Data\n\n
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Date
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2022
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English
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Concepts

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linear regression, linear algebra, work, values
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artificial neural networks, recurrent neural networks, convolutional neural networks, ann, cnn, rnn, logistic regression, long short-term memory, tensorflow, python, keras., convolution operation, 1st row, 2nd row, max pulling, maximum number, edge detector, average pulling, matrix multiplication, output matrix, edge, operation, output, preserved, start, taking, move, matrix, information, tensor, defined, give, finish, sort, means, kind, cell, entry
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\n\" data-title=\"Get Full Access\" data-container=\"body\" rel=\"popover\" tabindex=\"0\" aria-label=\"Play video: Fundamentals of Neural Networks - Convolutional Operation\" href=\"/subscription/new\">\"Fundamentals\n\n
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