Presently, I rank among the top 50 experts on AI StackExchange, showcasing my commitment to staying at the forefront of industry knowledge([login to view URL]). Additionally, I am nearing completion of a groundbreaking paper on a sports prediction architecture, for which I am the sole author. This project combines innovative deep learning models with classic ML techniques, underlining my ability to integrate diverse methodologies for impactful solutions.
My problem-solving proficiency spans a wide spectrum, from computer vision to deep reinforcement learning, with a focus on algorithmic trading. I possess a robust foundation in linear algebra, probability, set theory, and various mathematical concepts. My expertise in ML algorithms encompasses both traditional methods (linear CoF, random forests, SVMs) and cutting-edge approaches (capsule networks, ANNs, pointer networks, and transformers). Therefore, I can reason about the optimality of a particular architecture with respect to a given problem domain.
My versatility extends to applying deep learning to tabular problems and predicting stress on unlabeled structured data in an unsupervised context.
I am intimately familiar with the latest NLP models used in the field today(Llama 3,GPT-4,Bard), and have expressly used them for this purpose in multiple projects(as well as prompting techniques) . I look forward to our discussion! Thanks,
Austin