Frequently Asked Questions

The course has 12 modules, each following the same structure: pre-class preparation, lectures, quiz, live session, practice, and assignment. All materials are on this website, while Canvas handles quizzes, assignments, and progress tracking.

Yes! All course materials on this GitHub Pages site remain accessible indefinitely. You can bookmark and return to any content for future reference.

This site contains all learning materials (videos, readings, code). Canvas handles course management (quizzes, assignments, grades, discussions, deadlines).

Not necessarily! You can use Google Colab for free GPU access. For local development, any modern computer works, though a GPU helps with larger models.

The course covers both frameworks. Most examples use PyTorch, but TensorFlow alternatives are provided. Choose based on your preference or career goals.

Use Google Colab as a backup - it requires no installation. Check the setup guide for troubleshooting, or ask for help in Canvas discussions.

All assignments are submitted through Canvas. You'll typically submit Jupyter notebooks or Python files, along with a brief report.

Individual assignments must be completed alone, but you're encouraged to discuss concepts with classmates. The final project may have a group option.

Check the Canvas syllabus for late submission policies. Contact the instructor immediately if you have extenuating circumstances.

Use Canvas discussion forums for course-related questions so everyone can benefit. Email the instructor for private matters or urgent issues.

Yes! Check Canvas for the current office hours schedule. Both in-person and virtual options are typically available.

Discussion forum posts are usually answered within 24 hours. Email responses within 48 hours. Urgent issues are prioritized.