The ability to try many different neural network architectures to address a problem is what makes deep learning really powerful, especially compared to shallow learning techniques like linear regression, logistic regression etc. In this assignment, you will:
- Explore the CIFAR10 dataset: https://www.cs.toronto.edu/~kriz/cifar.html
- Set up a training pipeline to train a neural network on a GPU
- Experiment with different network architectures & hyperparameters
Steps to complete the assignment
- Fork & run this notebook: https://jovian.ml/aakashns/03-cifar10-feedforward
- Fill out all the ??? in the notebook to complete the assignment, and commit the final version to Jovian
- Submit your assignment here: https://forms.gle/nk2PybWoFDf44bwa9
- (Optional) Write a blog post on one of the topics suggested at the end of the notebook
- (Optional) Share your work with the community on the Share Your Work Here - Assignment 3 thread
CORRECTION: There was a small error in the starter notebook where the full
dataset was used for creating training, validation & test data loaders. This has been fixed now, please “fork” the starter notebook again if have already forked the notebook.
Make sure to review the material from Lecture 3 before starting the assignment. Please reply here if you have any questions or face issues. The recommended platform for writing your blog post is medium.com .