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Stanford University CS231n: Convolutional Neural Networks for Visual Recognition

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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.

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