Convolutional neural network: Difference between revisions
(Created page with "Convolutional Neural Network Primarily used for image tasks such as computer vision or generation") |
No edit summary |
||
| Line 1: | Line 1: | ||
Convolutional Neural Network | Convolutional Neural Network<br> | ||
Primarily used for image tasks such as computer vision or generation | Primarily used for image tasks such as computer vision or image generation, | ||
though they can be used anywhere you have a rectangular grid with spatial relationship among your data. | |||
==Convolutions== | |||
[https://pytorch.org/docs/stable/nn.html#convolution-layers Pytorch Convolution Layers]<br> | |||
Here, we will explain 2d convolutions.<br> | |||
Suppose we have the following input image:<br> | |||
and the following 3x3 kernel:<br> | |||
For each possible position of the 3x3 kernel over the input image, | |||
we perform an element-wise multiplication (<math>\odot</math>) and sum over all entries to get a single value. | |||
===Stride=== | |||
===Padding=== | |||
===Dilation=== | |||
===Groups=== | |||
==Types of Convolutions== | |||
===Transpose Convolution==== | |||
Revision as of 18:57, 21 February 2020
Convolutional Neural Network
Primarily used for image tasks such as computer vision or image generation,
though they can be used anywhere you have a rectangular grid with spatial relationship among your data.
Convolutions
Pytorch Convolution Layers
Here, we will explain 2d convolutions.
Suppose we have the following input image:
and the following 3x3 kernel:
For each possible position of the 3x3 kernel over the input image, we perform an element-wise multiplication (\(\displaystyle \odot\)) and sum over all entries to get a single value.