Deep Learning Image Classification

Project Overview

This project explored the optimization of convolutional neural networks for image classification tasks. Using the classic cats vs. dogs dataset, I investigated how architectural choices and hyperparameter selection affect both performance and computational efficiency.

Key Aspects

• Designed and implemented a complete deep learning pipeline
• Conducted systematic experiments to understand the relationship between network architecture and performance
• Explored the balance between accuracy, model size, and computational requirements
• Applied transfer learning to leverage pre-existing knowledge representations

Sustainability Considerations

The project included a focus on computational efficiency, recognizing that state-of-the-art deep learning often comes with significant environmental costs due to energy consumption. By optimizing hyperparameters, I achieved comparable results with reduced computational requirements.

Technologies Used

• Python with TensorFlow for implementation
• Systematic experimentation methodology
• Data augmentation for improved model robustness