Convolutional Neural Networks (CNN)

machine-learning
cnn
convolution
max-pooling
relu
pooling
flattening
full-connection

(Team) #1

Our brain processes things in a pictorial fashion. It tries to look for features and identify or classify objects in our surroundings. Since, our aim with neural networks is to mimic the human brain, a convolutional neural network (CNN) is mechanised such that it looks for features in an object.

CNN has the following five basic components:

  • Convolution : to detect features in an image
  • ReLU : to make the image smooth and make boundaries distinct
  • Pooling : to help fix distored images
  • Flattening : to turn the image into a suitable representation
  • Full connection : to process the data in a neural network


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