Teaching AI visually and practically!
Welcome to the Computer Vision (CV) course! This guide will help you get started with the course material, set up your coding environment, and navigate the GitHub-based LMS efficiently.
docs/
: LMS documentation, GitHub Pages content, and course codebase guidelines.lectures/
: Weekly lecture notes, slides, and code examples.assignments/
: Problems and instructions for weekly submissions.projects/
: Mini and final project descriptions, starter templates, and rubrics.datasets/
: Links and instructions for downloading and organizing datasets.tools/
: Environment setup guides, library requirements, cheat sheets, and config scripts.resources/
: Recommended books, research papers, videos, tutorials, and references.checkpoints/
: Review folders with weekly recap questions and learning outcome checklists.lectures/
.slides.pdf
: Lecture presentationnotes.md
: Written explanation of conceptscode/
: Python examples and hands-on practice.ipynb
notebooks using Jupyter or Google Colab.assignments/
folder.Need help with PRs? See GitHub Guide to Pull Requests
projects/project_ideas.md
This is your space to:
pip
(Python package manager)Install these with pip
:
pip install numpy matplotlib opencv-python scikit-image pillow flask
Optional (but useful):
pip install seaborn pandas tqdm torch torchvision torchaudio
You can open .ipynb
files directly in Colab:
Watch this repository or discussions/Annoncements to get updates on:
Keep an eye on the checkpoints/ folder for weekly review sessions and quizzes.
Enable GitHub notifications or check Announcements.
Instructor:
Purushotham Mailapalli
Assistant Professor, JBIET College, Hyderabad
π§ purus15987@gmail.com | π GitHub | π LinkedIn |
Office Hours: 9.30AM - 4.15PM
Github Discussions: Anytime
Letβs build intelligent systems, one pixel at a time. Good luck!
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