This repository contains a TensorFlow implementation of a Convolutional Neural Network (CNN) for classifying images from the CIFAR-10 dataset. The CIFAR-10 dataset consists 32x32 colored images separated into 10 classes. The classes include airplanes, automobiles, birds, cats, deer, dogs, frogs, horses, ships, and trucks.
The model features two convolutional layers, which are crucial for extracting detailed features from images, essential for effective classification. The preprocessing steps include normalizing the pixel values to a range of 0 to 1, enhancing the neural network's performance.
The project was a practical exercise to learn about the workings and implementation of convolutional neural networks. It follows a tutorial by CodeBasics, which is aimed at beginners looking to get started with CNNs in TensorFlow.