Key insights:

1. The literature review provided insights into deep learning advancements, informing the project's approach.
2. Experimentation led to an effective neural network architecture for image classification.
3. Hyperparameter tuning was crucial for optimizing model performance.
4. The trained model showed strengths in classification but had limitations, suggesting room for improvement.
5. Future research could explore alternative architectures and data modalities to enhance performance.