My Projects
A collection of my AI/ML projects and research work
Neural Network Framework
Built a complete neural network framework from scratch using only Python and NumPy. Implemented backpropagation, various activation functions, and optimization algorithms including SGD, Adam, and RMSprop. This project demonstrates a deep understanding of the mathematical foundations of deep learning.
Key Features:
- Modular architecture for easy layer composition
- Support for multiple activation functions (ReLU, Sigmoid, Tanh)
- Various optimization algorithms
- Batch normalization and dropout regularization
- Comprehensive testing suite
Real-Time Object Detection System
Developed a real-time object detection application using YOLO architecture and PyTorch. Optimized for edge devices with model quantization and pruning techniques. Achieved 60+ FPS on standard hardware while maintaining high accuracy.
Key Features:
- Real-time detection with high frame rates
- Custom trained on domain-specific datasets
- Model optimization for edge deployment
- Web interface for easy interaction
- REST API for integration
Advanced Sentiment Analysis Engine
Created a sophisticated sentiment analysis system using transformer architecture (BERT). Fine-tuned on multiple datasets for social media text classification, achieving state-of-the-art performance on benchmark datasets.
Key Features:
- Multi-class sentiment classification
- Aspect-based sentiment analysis
- Emotion detection capabilities
- Support for multiple languages
- Interactive web dashboard
Medical Image Segmentation
Implemented U-Net architecture for medical image segmentation tasks. Focused on tumor detection and organ segmentation from MRI and CT scans. Collaborated with medical professionals to validate results.
Key Features:
- High-precision segmentation masks
- Data augmentation pipeline
- Ensemble model for improved accuracy
- Visualization tools for medical professionals
- DICOM file support
Reinforcement Learning Agent
Developed RL agents using Deep Q-Networks (DQN) and Policy Gradient methods. Trained agents to play complex games and solve optimization problems. Implemented experience replay and target networks for stable learning.
Key Features:
- DQN and Double DQN implementations
- Policy gradient methods (A3C, PPO)
- Custom environment support
- Training visualization dashboard
- Hyperparameter tuning framework
Custom Language Model
Built a GPT-style language model from scratch to understand transformer architecture. Implemented multi-head attention, positional encoding, and trained on custom datasets. Capable of text generation and completion tasks.
Key Features:
- Transformer architecture implementation
- Multi-head self-attention mechanism
- Custom tokenizer and vocabulary
- Various decoding strategies (greedy, beam search)
- Fine-tuning capabilities