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 Mo Patel and Neejole Patel walk you through using PyTorch to build a content recommendation model.
Mo Patel is an independent deep learning consultant advising individuals, startups, and enterprise clients on strategic and technical AI topics. Mo has successfully managed and executed data science projects with clients across several industries, including cable, auto manufacturing, medical device manufacturing, technology, and car insurance. Previously, he was practice director for AI and deep learning at Think Big Analytics, a Teradata company, where he mentored and advised Think Big clients and provided guidance on ongoing deep learning projects; he was also a management consultant and a software engineer earlier in his career. A continuous learner, Mo conducts research on applications of deep learning, reinforcement learning, and graph analytics toward solving existing and novel business problems and brings a diversity of educational and hands-on expertise connecting business and technology. He holds an MBA, a master’s degree in computer science, and a bachelor’s degree in mathematics.
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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