Resources for students in the Udacity's Machine Learning Engineer Nanodegree to work through Stanford's Convolutional Neural Networks for Visual Recognition course.
Christopher Olah's blog on Neural Networks
Backpropagation and batchnorm regularization
How Convolutional Neural Networks Work
A place for students to share code, double check their work and troubleshoot problems.
Honor Code
We ask that you not look at this section unless you have finished the assignment or have spent quality time figuring out the problem. Out of respect for Stanford releasing their class to the public, please do not abuse this policy. Try your best to figure out the solutions on your own, read the Reddit forms, or ask questions on our Slack channel before looking at solution code. Personally, I like to spend a few days trying to figure something out before looking at solution code.
Contributions
If you have finished the assignment, we hope you can add your answers to the repository for other students. If you choose to do so, please fork the repo and add a folder with your alias and put the assignments inside your folder. Always do a git pull before you do a git push so that there are no merge conflicts
git clone https://github.com/machinelearningnanodegree/stanford-cs231.git
cd stanford-cs231/solutions
mkdir yourdirectoryname
Copy the assignment1 into yourdirectoryname
cd /stanford-cs231/
cp -R /stanford-cs231/assignments/assignment1 /stanford-cs231/solutions/yourdirectoryname/assignment1
If there is no directory called assignment1 in your directory it will be automatically created with above
Download the dataset by:
cd yourdirectoryname/assignment1/cs231n/datasets
run ./get_datasets.sh inside the datasets directory
After everything is downloaded
cd into the assignment1 directory backwards
and then run ipython notebook
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Thanks to Contributors: kvn219, LevinJ, pranayaryal, & vijendra-rana
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