Project Title:
Dog Breed Classification Using Deep Learning

Project Description:
This project uses a deep learning model to classify various dog breeds from images. The model was trained using a dataset consisting of multiple dog breed images, and the goal is to predict the breed of a dog based on visual features.

Key Components:
Dataset: The dataset used for this project includes images of different dog breeds, which have been pre-labeled for training and validation purposes. The data is processed to be suitable for deep learning models.

Model: A Convolutional Neural Network (CNN) is employed to learn hierarchical features from the images. The architecture is designed to handle the complexity of visual data, improving accuracy in classification.

Data Preprocessing: Data augmentation techniques such as rotation, zooming, and flipping are applied to the images to prevent overfitting and enhance the model's generalizability.

Tech Stack: Python, TensorFlow, Keras, and OpenCV are used in the implementation, along with Matplotlib for visualizing training results.

Objective:
The objective of this project is to apply deep learning techniques to the task of image classification, specifically to the challenging problem of identifying dog breeds from images. The model’s accuracy and effectiveness are evaluated using common performance metrics like accuracy and loss.

Results:
The trained model achieves high accuracy on test data, demonstrating the potential of deep learning for real-world image classification tasks. You can view detailed results in the accompanying Jupyter notebook, which includes both visual and statistical analysis of model performance.