Aabha Deep Learning Foundations

Extract the ZIP, keep the folder together and open index.html. It is an offline reading guide, not a Python runtime. Read SYLLABUS.md, then Lessons 01–12 in order; attempt each practice task before opening solutions.html.

The practical core uses actual PyTorch on CPU. It needs Python 3.12 and an initial package installation; no GPU, paid API, account, pretrained weight download or real personal data is required. The CPU torch wheel alone is about 184 MB compressed. See SETUP.md for exact tested versions, platform-specific commands and the verified installation footprint. A normal Pyodide/browser Python lab does not provide this course’s PyTorch runtime. Reading and hand arithmetic work offline; completing the training exercises requires the separate Python runtime.

Start small, after setup:

python examples/01_tensors.py
python examples/02_backprop.py
python examples/03_miniloop.py
python -m unittest discover -s tests -v
python -m dlcourse.run --task all
python -m dlcourse.diagnostics

Use the interpreter from your virtual environment in place of python, as shown in SETUP. The first three examples are self-contained. The course modules must be run from the extracted package root.

Complete exercises/stages.py, then run python check_learner.py. This checks your code; the incomplete starter intentionally fails. solutions/stages.py is separate. The unit suite checks the reference implementation and is not a learner grade. For the capstone, edit exercises/capstone.py and run selection-only mode before the explicit final-assessment command in CAPSTONE.md.

Contents: 12 narrative lessons; 36 lesson exercises and 18 checkpoint questions; six small examples; staged learner/reference code; three generated datasets; diagnostics; original SVG visuals; capstone rubric; source register; measured validation evidence; offline reading/practice/solutions HTML; proposed integration manifest.

This is a source-only teaching handoff. No virtual environment, model checkpoint, downloaded wheel or build cache is included. Linux CPU execution is verified. macOS/Windows installation, browser layout/interaction, screen-reader behavior and a beginner learner pilot remain unverified. Nothing has been published or deployed. Do not describe this package as full transformer or LLM training mastery.