CS231n is a Stanford course on using neural networks to train visual recognition. It lasts 10 weeks and takes students through the process of designing and implementing a neural network that can identify visual classifications of objects.
Created by: Fei-Fei Li
Produced in 2017
Overall Score : 92 / 100
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- This is an elite class taught by an elite university. Completing this course denotes high-level proficiency in neural network design and implementation.
- Course is available for audit and sit-in.
- This is an active college course and comes with the professor access that entails.
- This is an advanced class and not readily accessible. Students need to know calculus and linear algebra in addition to multiple programming languages.
- Course is just plain hard.
- Course access is prioritized for the Stanford community.
Dr. Fei-Fei Li is a Professor in the Computer Science Department at Stanford University, and Co-Director of Stanford's Human-Centered AI Institute. She served as the Director of Stanford's AI Lab from 2013 to 2018. And during her sabbatical from Stanford from January 2017 to September 2018, she was Vice President at Google and served as Chief Scientist of AI/ML at Google Cloud. Dr. Fei-Fei Li obtained her B.A. degree in physics from Princeton in 1999 with High Honors, and her PhD degree in electrical engineering from California Institute of Technology (Caltech) in 2005. She joined Stanford in 2009 as an assistant professor. Prior to that, she was on faculty at Princeton University (2007-2009) and University of Illinois Urbana-Champaign (2005-2006).
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