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Convolutional Neural Network (CNN) in Machine Learning

Machine Learning  is one of the most used modern technologies in our world. Machine Learning  helps human a lot to do their jobs at ease. Today almost every big tech companies use Machine Learning  technologies to give their customers best services. The full form of CNN is Convolutional Neural Network. You must have seen some apps which recognize your face. The technology behind this function is convolutional neural network. We use this for mainly image recognition. This is an important technology in Machine Learning. Many companies use this technology. So, it will be very good for you if you know about this thing in details. You must have seen people who build their ML projects on face recognition. This thing is really cool and it will help you in your development in this field also. In this article we will discuss about Convolutional Neural Network in details.

What is Convolutional Neural Network?

It is one of the deep learning technologies by which we can recognize images. The main inspiration in its working is visual cortex. As visual cortex works by some neural cells, it also works in same pattern. It mainly takes inputs as images and then classifies them as their particular class. There are some CNN models like LeNet model, AlexNet model, ZFNet model, and GoogleNet model.

Example: 

This process is used in postal code detection. Suppose someone has written one number and it is very much unclear. There are lots of people who don’t write very clearly. So we can use convolutional neural network here. We can pass the images as input then we can get actual number as output. So, you can see any type of image recognition can be done by this convolutional neural network.

Some Basic Concepts of Convolutional Neural Network

  • Input: If the image consists of 32 widths, 32 height encompassing three R, G, B channels, it will hold the raw pixel ([32x32x3]) values of an image.
  • Convolution layer: You can imagine the importance of this layer from the name convolutional neural network itself. This layer mainly extracts different features from the input image. It is the first layer after taking input. It mainly does its function by mathematical operation. This operation uses two inputs like one kernel or filter and another is image matrix. It learns the image features and stores the relationship between different pixels of input image.
  • ReLU Layer: Rectified Linear Unit is a layer of CNN. It mainly makes the negative value of images as zero. It controls the higher quantities depending one threshold value.
  • Pooling: This layer is an important part of CNN. It mainly works by reducing the size of image stack.
  • Locally Connected: It can be defined as a regular neural network layer that receives an input from the preceding layer followed by computing the class scores and results in a 1-Dimensional array that has the equal size to that of the number of classes.
  • Strides: Strides represent the number of pixels in input image which we have to shift at a time. If the number of strides is equal to 1 then we have to shift 1pixel in input image. If the number of strides is 2 then similarly we have to shift 2 pixels in input image.

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