Implementing CNNs for Image Classification in Python

CNNs are commonly used in DL architectures for image recognition and analysis. The CNN architecture effectively identifies patterns in image data without requiring explicit feature engineering.

In the following steps, we're building a Convolutional Neural Network (CNN) for image recognition tasks, particularly utilizing the Keras library. The process involves importing necessary libraries, loading the MNIST dataset, reshaping images, normalizing data, building the CNN model, training the model, and evaluating its performance on test data. This sequence demonstrates how to construct and train a CNN using Keras for accurate image classification.

Step 1: Importing libraries

Keras is an open-source, high-level DL library with an easy-to-use interface for building neural networks. It allows developers to quickly prototype and experiment with different models, including CNNs. It also includes a collection of in-built layers, loss functions, activation functions, and optimization methods.

The following code illustrates how to import the necessary libraries using Keras for image classification:

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