Adapting GANs for Image Classification

• Used StarGAN architecture to classify images across multiple domains.
• Achieved an F1 Score of 0.83, Precision of 0.9917, and Recall of 0.8051 on the CelebA dataset.
• Trained model over 500,000 iterations using PyTorch and Adam optimizer.
• Applied adversarial loss, classification loss, and gradient penalty for balanced training.
• Optimized performance with specific hyperparameters ensuring stable performance.

• Developed and deployed text corpus deduplication using a suffix array algorithm on MapReduce, boosting assessor F1-score from 0.77 to 0.82.
• Trained a classifier to improve benchmark coverage, enhancing dataset relevance for pretraining tasks.
• Created a dataset augmentation pipeline with Back Translation, increasing pretraining robustness and generalization.
• Enhanced document parsing quality, resulting in 3% faster model convergence and improved resource efficiency.

• Developed AI/ML solutions to help businesses optimize processes and improve efficiency.
• Implemented fraud detection systems to predict anomalies in transactions.
• Developed demand forecasting models to optimize inventory and supply chains.
• Created customer churn prediction models to analyze data and retain users.
• Built recommendation systems to personalize content and products.
• Conducted sentiment analysis using NLP to detect sentiments.

I used vision transformers, data augmention,face detector and deployed the model on AWS SagaMaker

• Developed a real-time Sign Language Action Detection System achieving 90% accuracy.
• Integrated face, hand, and body landmark detection for precise sign recognition.
• Trained LSTM neural network reaching 85% accuracy, improving performance by 25%.
• Enhanced accessibility for individuals with hearing and speech impairments.
• Enabled applications in education and assistive communication tools.