Deep Learning Investigation

 

๐Ÿ” 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.

๐Ÿง  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).

๐Ÿ”„ 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.

๐Ÿงช 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 preprocessing, and certified robustness.

๐ŸŒ 5. Deep Learning for Sustainability

Description:
Examine how deep learning can optimize energy usage, predict climate patterns, or enhance smart grid systems. Investigate model efficiency and environmental impact (carbon footprint).

๐Ÿงฌ 6. Deep Learning in Biomedical Imaging

Description:
Apply DL to tasks like tumor detection, organ segmentation, or disease classification in MRI, CT, and X-ray images. Use datasets like NIH ChestX-ray14 or BraTS for experiments.

๐Ÿงพ 7. Model Compression and Optimization

Description:
Study methods such as pruning, quantization, and knowledge distillation to make deep networks lightweight and faster, especially for edge devices or mobile deployment.

๐Ÿค– 8. Reinforcement Learning with Deep Networks

Description:
Investigate how deep neural networks are integrated into reinforcement learning frameworks (e.g., DQN, PPO) for applications in robotics, games, and autonomous navigation.

๐Ÿ“š 9. Dataset Bias and Ethical Implications

Description:
Explore how biases in training datasets affect DL model predictions. Study fairness metrics, bias mitigation techniques, and responsible AI practices.

๐Ÿง  10. Self-Supervised and Unsupervised Deep Learning

Description:
Investigate frameworks like SimCLR, BYOL, and Autoencoders to understand learning representations without labeled data. Focus on applications in low-resource environments.

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