The primary aim of this project is to demonstrate the efficacy of transfer learning in improving the accuracy of neural network models compared to traditional learning methods. By employing a simplified architecture that integrates high-level features extracted from a pre-trained AlexNet, the project seeks to harness the strong feature-detecting capabilities of deep learning networks previously trained on large datasets. This approach is tested against more complex models that do not utilize transfer learning, with the goal of illustrating significant improvements in model performance and efficiency. The project involves rigorous training, hyperparameter tuning, and comparative analysis to validate the advantages of transfer learning in practical applications.
Data will be provided upon individual request for the purpose of project replication. Please contact me for access.