Convolution Neural Network
Convolution Neural Network
Convolution Neural Network or CNN is a part of Deep learning which works under the basics of Neural Network of Human Brain. They are used in operations that involve Computer Vision. As the range of Artificial Intelligence is expanding enormously, we can see the application of Convolution Neural Network in our everyday life. These play important role which ranges from Facebook Automatic Tagging, Face Unlock, OCR to Driverless cars.
Image classification takes in an image as input and returns an output which is a class or probability with the help of deep learning algorithm which is Convolution Neural Network. Let’s see how a human observe an image. There are certain features that our brain relates an object to. We can differentiate two images of similar object based these features that we have learned. For many years we have trained our brain to differentiate such images using the features. Same idea works behind the Convolution Neural Network.
When we input an image to our model for it, it is basically a collection of matrices. For our model, an image is a matrix of pixels. When we pass a coloured image, it will be seen as 3D array with RGB channel ranging from 0 to 255. If we pass black and white image, we will get a 2D array with binary values.
The basic pipeline of Convolution Neural Network starts with the first step of providing an image as an input, then performing convolution operation to produce an activation map, applying pooling layer , then, Activation function is usually chosen as ReLu and is applied to avoid non-linearity. After that, the last output is flatten into one linear vector and is then passed to a fully connected Artificial Neural Network. This fully connected layer will provide a probability for each class and repeat these processes to get well defined trained weights and feature detectors.
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