For the course project, you will pick any dataset of your choice and apply the concepts learned in this course to train deep learning models end-to-end with PyTorch, experimenting with different hyperparameters & metrics.
Starter Notebook: There is no starter notebook for the course project. Please use the “New Notebook” option from your Jovian profile to create a new notebook, “Run on Colab” to execute it, and “jovian.commit” to record versions.
Guidelines for completing the project
For the course project, you will pick a dataset of your choice and apply the concepts learned in this course to train deep learning models end-to-end with PyTorch, experimenting with different hyperparameters & metrics.
- Find a dataset online (see the “Where to Find Datasets” section below)
- Understand and describe the modelling objective clearly
- What type of data is it? (images, text, audio, etc.)
- What type of problem is it? (regression, classification, generative modelling, etc.)
- Clean the data if required and perform exploratory analysis (plot graphs, ask questions)
- Define a model (network architecture)
- Pick some hyperparameters
- Train the model
- Make predictions on samples
- Evaluate on the test dataset
- Save the model weights
- Record the metrics
- Try different hyperparameters & regularization
- Conclusions - summarize your learning & identify opportunities for future work
- Publish and submit your Jupyter notebook
- (Optional) Write a blog post to describe your experiments and summarize your work. Use Medium or Github pages.
Example notebooks for reference: