Computer vision is a key component in the artificial intelligence revolution. Assisted by deep learning, object detection allows automotive applications to make key navigation, guidance, and driving decisions based on the field of view and the objects within it to avoid collisions and navigation errors. Laura Froelich and Mo Patel demonstrate how to train deep learning models for object detection using publicly available transportation datasets, focusing on building and training a convolutional neural network (CNN) using techniques from a combination of leading-edge research papers to achieve the best training results.
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.
Laura Froelich is a data scientist at Think Big Analytics, a Teradata Company, where she is dedicated to utilizing data to discover patterns and underlying structure to enable optimization of businesses and processes, particularly through deep learning methods. Previously, she was part of a research group investigating nonspecific effects of vaccines using survival analysis methods. Laura holds a PhD from the Technical University of Denmark. For her dissertation, Decomposition and Classification of Electroencephalography Data, Laura used unsupervised decomposition and supervised classification methods to research brain activity and developed rigorous, interpretable approaches to classifying tensor data.
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