As deep learning has gained popularity and evolved into mainstream software development, there’s been a corresponding rise in frameworks to build deep learning applications. On a simple level, these frameworks can be classified by the define-and-run and define-by-run design patterns. One advantage define-by-run frameworks have is the dynamic nature of the computation graph allowing for flexibility in modeling.
PyTorch, a deep learning framework largely maintained by Facebook, is a design-by-run framework that excels at modeling tasks where flexible inputs are critical, such as natural language processing and event analysis. You’ll gain hands-on experience with PyTorch, as Neejole Patel walks you through using PyTorch to build a content recommendation model.
Neejole Patel is a sophomore at Virginia Tech, where she is pursuing a BS in computer science with a focus on machine learning, data science, and artificial intelligence. In her free time, Neejole completes independent big data projects, including one that tests the Broken Windows theory using DC crime data. She recently completed an internship at a major home improvement retailer.
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