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. You will prepare a project report in the form of a blog post to summarizing your results & findings (the blog post is mandatory).
Starter Notebook: There is no starter notebook for the project. Please use the “New Notebook” option from your Jovian profile to create a new notebook.
Guidelines for completing the project
- Find a dataset online (either download and existing dataset or create one from web scraping, Google images etc.)
- 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.)
- Exploratory data analysis - explore the data by plotting graphs and answer any questions you may have
- Modeling - try 4-5 approaches
- 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
- Conclusions - summarize your learning & identify opportunities for future work
- Write a blog post to describe your experiments and summarize your work - this is the final submission. Use Github pages or Medium.
More Details will be added soon