I'm a data scientist with a strong theoretical understanding of the inner workings of machine/deep learning algorithms and the underlying mathematics, and equipped with the best-practices for utilizing these algorithms to solve real-world problems.
- Classical Machine learning: regression, trees, K-NN, SVM, ensembles, K-means, PCA, etc.
- Computer Vision: image classification, semantic segmentation, object detection using architechtures such as ResNet, Inception, FCN, UNet, DeepLab, Faster RCNN, RetinaNet, and YOLO.
- Deployment using AWS.
- Exploratory data analysis.
- Web scraping.
- Building APIs.
- Transformers and their applications to NLP and computer vision
- Object tracking using Yolo and deepsort
- Transformer illustrated
- Transformers from scratch
- Natural Language Processing with Transformers
- The annotated Transformer
- Attention Mechanisms in Computer Vision: A Survey