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Description
📌 Description
Implement a Convolutional Neural Network (CNN) for image classification on a well-known dataset (e.g., CIFAR-10, MNIST, or any publicly available dataset). This task will involve:
- Loading and preprocessing the image dataset
- Performing exploratory data analysis (EDA) to understand class distribution
- Building and training a CNN using TensorFlow/Keras or PyTorch
- Evaluating model performance using accuracy, confusion matrix, and other metrics
- Visualizing model predictions and feature maps
🎯 Goals
- Understand and implement image classification using deep learning
- Work with real-world image data
- Learn how to optimize and fine-tune CNN models
🛠 Tech Stack
- Python
pandas
,numpy
,matplotlib
/seaborn
for data handling & visualizationTensorFlow
/Keras
orPyTorch
for deep learning
📖 Resources
✅ How to Contribute
- Comment on this issue to get assigned
- Fork the repository and create a new branch
- Implement the solution and test it
- Create a pull request linking this issue
Looking forward to your contributions! 🚀