Projects

1. PyTorch Transformer

PyTorch

• Implemented the Transformer model from the Attention Is All You Need paper by Vaswani et al. using PyTorch.
• Applied the Transformer model to various natural language processing tasks, such as machine translation and sentiment analysis.

2. PDF-Synopsis Generator

Python, Flask, HuggingFace

• SummarizeMaster is an AI-powered application designed to simplify reading by extracting concise summaries from PDF books.
• It utilizes advanced models BART to provide insightful book summaries efficiently.

3. Lung cancer prediction using Image-Segmentation, Equalization and Transfer learning

Tensorflow, OpenCV, Seaborn

• Implemented an automatic lung cancer detection system using deep learning techniques to improve accuracy and reduce diagnosis time.
• Employed image processing methods, including Histogram Equalization and Threshold Segmentation, to preprocess CT scan images for better contrast and segmentation.
• Developed and trained various deep learning models, including Convolutional Neural Networks (CNN), VGG16, VGG19, MobileNet, ResNet50, Xception, and InceptionV3, for lung cancer prediction,