Fundamentals of Deep Learning
Demonstrated proficiency in fundamental deep learning techniques including CNNs, data augmentation, transfer learning, and building computer vision models using PyTorch
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Demonstrated proficiency in fundamental deep learning techniques including CNNs, data augmentation, transfer learning, and building computer vision models using PyTorch
Demonstrated expertise in building and deploying Transformer-based NLP applications using PyTorch, BERT, NVIDIA NeMo, and Triton Inference Server
Demonstrated proficiency in applying prompt engineering techniques to build LLM-based applications using NVIDIA NIM, LangChain, and Llama 3.1
Demonstrated understanding of the epistemological development of AI, machine learning paradigms, key AI applications, and ethical implications of AI systems in contemporary society
Demonstrated proficiency in building web applications using TypeScript, including object-oriented programming, classes, functions, and higher-order functions