Image Classification Projects

This repository includes Jupyter Notebooks for various image classification tasks using Convolutional Neural Networks (CNNs). The notebooks cover different datasets and techniques in image classification.

Notebooks

1. CIFAR-10 Image Classification File: CIFAR_10_data_set_image_classification.ipynb Description: This notebook demonstrates the use of Convolutional Neural Networks to classify images from the CIFAR-10 dataset. The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 different classes. Created using: Google Colab Date: 3 months ago
2. Convolutional Neural Network for Image Classification File: Convolutional_Neural_Network.ipynb Description: Provides a comprehensive implementation of a CNN for image classification tasks. It covers different architectures and techniques for optimizing model performance. Created using: Google Colab Date: 2 months ago
3. Digit Classification using CNNs File: Digit_Classification_using_Convolutional_Neural_Networks.ipynb Description: Focuses on classifying handwritten digits using CNNs. This notebook typically utilizes the MNIST dataset, which contains 70,000 images of handwritten digits. Created using: Google Colab Prerequisites To run the notebooks, you will need:

Python 3.x TensorFlow/Keras NumPy Matplotlib Pandas Install the required libraries with pip:

bash Copy code pip install tensorflow numpy matplotlib pandas Usage Clone this repository to your local machine. Open the notebooks using Jupyter Notebook or Google Colab. Follow the instructions provided in each notebook to train and evaluate the models. License This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements The CIFAR-10 and MNIST datasets used in these notebooks are publicly available datasets for training machine learning models. TensorFlow and Keras libraries are used for building and training neural networks.