Charles Frye is a Developer Advocate at Modal Labs with expertise in deep learning and neural networks. A UC Berkeley Ph.D. graduate in Neuroscience, he transitioned from biology to computer science and statistics. He previously served as a Deep Learning Educator at The Full Stack and Weights & Biases, where he created educational content on machine learning and Python programming. He's known for his talent in explaining complex quantitative concepts to non-experts.
