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Inferact PyTorch practice bank

Aaron Pham

pytorch practice bank This is the miss-driven expansion pool for P01 through P28 and M01 through M10. Its forty cases add PyTorch API work, debugging, and runtime-shaped tensor transformations without increasing the default forty-two-hour route. After a canonical owner is clean, replace its next...

Inferact study route

Aaron Pham

study route The default route is fourteen days at three focused hours per day. It yields forty-two scheduled hours with 45% assigned to PyTorch model construction and mechanism repair. The baseline can compress this to seven days. Each day names a primary implementation block that fits inside...

NVIDIA coding study route

Aaron Pham

study route The goal is fast recognition followed by correct code under pressure. Every block uses the same loop: Name the pattern and invariant before typing. Write two small examples and one hostile edge case. Implement without an editorial. Test empty, singleton, duplicate, boundary, and...

study

Aaron Pham

study plan The goal is a usable interview reflex: define the wire contract, hand-run examples, code the happy path, add guards, then prove split and roundtrip behavior. Every block follows the same small loop: read the route and diagram. solve one stub cold. run python3 test_problems.py. add the...

prep

Aaron Pham

Prep for a 60-minute algorithmic code screen on encoding and decoding. This kit is now meant to be learned in layers: shape: learn the mental model and one diagram. recipe: implement the smallest correct algorithm. guards: add the failure cases the interviewer will probe. recall: review notes.fc,...