CS231n: Convolutional Neural Networks for Visual Recognition

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.

( 5 Reviews )

Created by: Fei-Fei Li

Produced in 2017

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 92 / 100

Live Chat with CourseDuck's Co-Founder for Help

Need help deciding on a artificial intelligence course? Or looking for more detail on Fei-Fei Li's CS231n: Convolutional Neural Networks for Visual Recognition? Feel free to chat below.

Join CourseDuck's Online Learning Discord Community

Course Description

artificial intelligence Awards Best Free Course

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka "deep learning") approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Intermediate Level

Time: 19h 30m

Pros

Cons

- 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.

Instructor Details

Fei-Fei Li

4.6 Rating
5 Reviews

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).