Stanford University CS231n: Convolutional Neural Networks for Visual Recognition

The Course Project is an opportunity for you to apply what you have learned in class to a problem of your interest. Potential projects usually fall into these two tracks: One restriction to note is that this is a Computer Vision class, so your project should involve pixels of visual data in some form somewhere. E.g. a pure NLP project is not a good choice, even if your approach involves ConvNets. To get a better feeling for what we expect from CS231n projects, we encourage you to take a look at the project reports from previous years: To inspire ideas, you might also look at recent deep learning publications from top-tier conferences, as well as other resources below. For applications, this type of projects would involve careful data preparation, an appropriate loss function, details of training and cross-validation and good test set evaluations and model comparisons. Don't be afraid to think outside of the box. Some successful examples can be found below: ConvNets also run in real tim

Stanford University CS231n: Convolutional Neural Networks for Visual Recognition CS231n: Convolutional Neural Networks for Visual Recognition Course Project Warning: Details still subject to change --> Overview The Course Project is an opportunity for you to apply what you have learned in class to a problem of your interest. Potential projects usually fall into these two tracks: Applications. If you're coming to the class with a specific background and interests (e.g. biology, engineering, physics), we'd love to see you apply ConvNets to problems related to your particular domain of interest.

related reading

• Stanford University CS231n: Deep Learning for Computer Visioncs231n.stanford.edu
• Stanford University CS231n: Deep Learning for Computer Visioncs231n.stanford.edu
• CS231n Deep Learning for Computer Visioncs231n.github.io
• CS231n Deep Learning for Computer Visioncs231n.github.io
• Convolutional Neural Networks, Explained | Towards Data Sciencetowardsdatascience.com
• VS265: Neural Computation - Fall 2024 - Redwood Center for Theoretical Neuroscienceredwood.berkeley.edu
• Syllabuscs230.stanford.edu
• Lecture 1 - Part2 overview.pptxcs231n.stanford.edu
• CS231n Deep Learning for Computer Visioncs231n.github.io
• [2201.03545] A ConvNet for the 2020sarxiv.org
• ImageNet Classification with Deep Convolutional Neural Networksproceedings.neurips.cc
• Master of Science in Computer Vision - Robotics Institute Carnegie Mellon Universityri.cmu.edu