Deep Learning Investigation

🔍 1. Neural Network Architecture Exploration Description: Investigate various neural network architectures (CNNs, RNNs, LSTMs, Transformers) to understand their performance across different tasks such as image classification, language modeling, or time-series forecasting. 🧠2. Interpretability and Explainability Description: Study methods like SHAP, LIME, and attention visualization to interpret deep learning models. Focus on making black-box models more transparent, especially in high-stakes applications (e.g., healthcare, finance). 🔄 3. Transfer Learning and Fine-Tuning Description: Explore how pre-trained models (like BERT, ResNet, or CLIP) can be fine-tuned for specific downstream tasks. Evaluate performance gain and reduction in training time/data requirements. 🧪 4. Adversarial Robustness Description: Investigate how deep learning models respond to adversarial attacks (e.g., FGSM, PGD). Explore defense mechanisms such as adversarial training, input preprocessi...