Computer vision has led the artificial intelligence renaissance. In a few short years, we have seen advancements in computer vision research turned into production-level systems from web applications to mobile devices and the automotive industry, to name a few. Much of this progress is derived from convolutional neural networks (CNNs), and techniques such as object classification, localization and detection, tracking, and segmentation are foundational concepts for most vision-based applications today.
PyTorch enables researchers and practitioners to more easily build CNNs for computer vision. Released in early 2017, it has increasingly gained popularity in the computer vision community. Mo Patel offers an overview of computer vision fundamentals and walks you through PyTorch code explanations for notable objection classification and object detection models. There will be an equal balance of theory and hands-on PyTorch coding. You’ll also learn how to deploy PyTorch models into production via Caffe2.
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.
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