INTRODUCTION TO CNN IMPLEMENTATION

The first thing that comes to mind about image classification and recognition is how it works. Image classification is classifying the images in given categories based on their characteristics. Now, let’s talk about how these characteristics are extracted from the images. Here, CNN comes into play. CNN stands for Convolutional Neural Network, which is a type of deep learning neural network. CNN is mainly used for image analysis, image segmentation, and image classification, etc. Although it has the potential to do limitless work in several science fields.

 

Architecture of CNN

 

Cat Feature Extraction

CNN has several layers such as convolution layer, pooling layer, Fully Connected layer, Dense layer, etc. The convolution layer is responsible for extracting features from the images on the basis of which the classification is done. The work of pooling layers is to reduce the parameters of the image. All these layers connected together help in classifying an image.

As a beginner, Keras can help you to quickly start your deep learning career. Keras is a deep learning API written in Python. You should have the basic knowledge about how to import necessary libraries like NumPy, pandas, and matplotlib. 

These important steps will help you to implement CNN:

  1. Import necessary libraries.
  2. Load the Dataset.
  3. Perform Data Augmentation.
  4. Define and compile Keras CNN models like VGG, ResNet, InceptionNet, etc.
  5. Train the model.
  6. Evaluate Keras Model.
  7. Make Predictions.

 

Suhail Ahmed

Data Scientist

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