I specialize in developing intelligent systems at the intersection of Large Language Models (LLMs), Agent AI, Reasonng with RAG and Multimodal Vision-Language architectures. My current work focuses on creating AI solutions with advanced reasoning capabilities that bridge textual understanding, visual perception, and decision-making processes.
My technical journey began through intensive self-directed study, combining resources from leading platforms (fast.ai, DeepLearning.AI, Coursera) with university coursework from UC Berkeley, Stanford, and IBM. This hybrid learning path gave me a unique perspective on both theoretical foundations and practical implementations in Machine Learning and Deep Learning.
I remain deeply grateful to educational pioneers like Jeremy Howard, Andrew Ng and Andrej Karpathy, whose open-access teaching philosophies democratized AI education. Their mentorship-by-proxy shaped my approach to building accessible, explainable AI systems.
Most projects are implemented in Python using frameworks like PyTorch, TensorFlow, Keras, and Hugging Face Transformers, along with advanced CNN/RNN architectures, developed in environments such as Google Colab and Visual Studio Code.
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Feel free to explore my repositories and connect if you're interested in collaboration or discussions on the latest in AI research!