I'm a Machine Learning & AI Engineer with over 4 years of hands-on experience developing and deploying intelligent systems for real-world applications. My expertise spans Natural Language Processing (NLP), Computer Vision (CV), and Reinforcement Learning. I am passionate about leveraging cutting-edge technologies to solve complex challenges and drive innovation.\
🚀 My Skills
- Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras, Hugging Face
- Natural Language Processing (NLP): Text classification, Named Entity Recognition, Transformer models (BERT, GPT), Text generation
- Computer Vision (CV): Object detection, Image classification, Image segmentation, OpenCV, YOLO, Faster-RCNN
- Reinforcement Learning: Q-Learning, Deep Q Networks (DQN), Proximal Policy Optimization (PPO)
- Backend Engineering: REST APIs, Microservices architecture, Cloud computing (AWS, GCP)
- Programming Languages: Python, Golang, JavaScript, Scala
- DevOps & Tools: Docker, Kubernetes, CI/CD pipelines
- Data Handling & Databases: SQL, NoSQL, Pandas, Numpy
🔥 What I'm working on
- Developing end-to-end AI systems that solve real-world problems in NLP, CV, and reinforcement learning.
- Building and optimizing backend systems to support scalable AI applications.
- Contributing to open-source projects related to AI and ML.
- Experimenting with AI ethics and ensuring fairness and transparency in machine learning models.
💡 What drives me
- Building innovative solutions that push the boundaries of AI and ML.
- Working on complex problems where AI can make a real impact on society.
- Continuously learning and improving my skills in both AI and software engineering.
📈 Projects
- Real-time Hand Gesture Recognition with 3D CNNs: PyTorch implementation of the Real-time Hand Gesture Detection and Classification Using Convolutional Neural Network and Resource Efficient 3D Convolutional Neural Network, codes and pretrained model.
- KAN-GPT: The PyTorch implementation of Generative Pre-Trained Transformer (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling.
- Music Genre Classification: This project aims to classify music genres. CNN architecture and the GTZAN dataset were used for model training. Finally, a Web Application was made with Flask.
🌱 Let's Connect