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🧠 Neural Networks
Convolutional

Updated at 2018-07-19 23:10

Convolutional neural networks (convnet or cnn) are deep, feed-forward artificial neural networks, most commonly applied to analyzing visual imagery.

Most CNNs have one main pair of operations:

  1. Convolution: slide a filter across an image e.g. if you are looking for vertical lines, you slide a vertical line filter across the image, you get back feature maps showing where the vertical lines are.
  2. Pooling: helps to everything to fit in memory and removes insensitivities e.g. vertical lines close to each other.

What the convnet architecture usually looks like:

Image > Convolution > Pooling > Convolution > Pooling > Fully-connected > Output

CNNs usually learn the training data in layers in the following way:

  • 1st Layer: detects edges
  • 2nd Layer: detects corners and curves
  • 3st Layer: detects square, triangle and circle
  • Rest of the Layers: detects special characteristics of the use-case

Sources