Drowsiness Detection using YOLO
Project aims to develop a computer vision system using YOLO network to detect drowsiness by analyzing facial landmarks and eye movements. It enhances safety, especially in activities like driving, by providing timely alerts to prevent accidents.
- PyTorch: Deep learning framework library
- OpenCV: Open source library for computer vision
- LabelImg: Annotation tool for computer vision.
- Matplotlib: Plotting library for Python data.
Interpretation of Convolutional Neural Networks (CNNs)
Exploring techniques to interpret Convolutional Neural Networks (CNNs), popular deep learning models for image classification. Techniques include GradCAM, Lime, Rise, and more.
- Pandas: Data manipulation and analysis
- Seaborn: Data visualization
- Tensorflow: Deep Learning framework library
- OpenCV: Open source library for computer vision
Interpretable Heart Disease Machine Learning Classifier
An analysis of clinical data to predict heart disease in patients. The solution focuses on creating an ML model capable of predicting the disease and showing how the algorithm arrived at its prediction.
- Pandas: Data manipulation and analysis
- Seaborn: Data visualization
- scikit-learn: Machine learning models
- PyCaret: Automated machine learning
- SHapley Additive exPlanations (SHAP): Explainable AI
English to Cherokee translator using Transformers
Using Transformers to create an English to Cherokee translator, exploring different pre-trained architectures like BERT and Helsinki NLP.
- Pandas: Data manipulation and analysis
- Tensorflow: Deep Learning framework library
- Simple Transformers: Natural language processing library
- Hugging Face: Open source data science and machine learning platform