Which frameworks are supported?

While the skill encourages minimal dependencies, it is optimized for core libraries like NumPy and PyTorch fundamentals rather than high-level abstractions.

Does this skill provide copy-paste code for papers?

No, the philosophy of this skill is to avoid copy-pasting. It guides you to write the implementation yourself to ensure deep conceptual and practical understanding.

Is this skill suitable for complex architectures like Transformers?

Yes, it includes specific implementation tips for Transformers, Reinforcement Learning, Generative Models, and Computer Vision papers.

How does the debugging gauntlet work?

It provides a series of tests including the Overfit Test (can you memorize a single batch?), Gradient Checks (are gradients flowing?), and Learning Rate Sweeps.

Implement Paper From Scratch

Name: Implement Paper From Scratch
Author: GhostScientist

byGhostScientist

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데이터 과학 및 ML

Guides the step-by-step implementation of research papers from scratch to ensure deep understanding and technical reproducibility.

소개

This skill provides a rigorous, structured framework for developers and researchers looking to translate academic papers into working code. Instead of relying on high-level wrappers or copy-pasting reference implementations, it emphasizes building from fundamentals using core libraries like NumPy or PyTorch. The skill leads you through a four-phase process: pre-implementation analysis, scaffolded development (data, model, loss, training, and evaluation), a dedicated debugging gauntlet, and reflective checkpoint questions. It is designed to bridge the gap between theoretical understanding and practical engineering by focusing on numerical stability, shape verification, and core logic.

주요 기능

Guidance on minimal dependency implementation using core frameworks
1 GitHub stars
Conceptual checkpoint questions to verify understanding at every stage
Structured multi-phase implementation roadmap for research papers
Rigorous debugging gauntlet including overfit tests and gradient checks
Domain-specific tips for Transformers, RL, Generative Models, and CV

사용 사례

Reproducing a new Deep Learning architecture from an arXiv paper
Building custom model layers or loss functions without high-level wrappers
Deepening AI knowledge by implementing core algorithms from first principles

소개

주요 기능

Guidance on minimal dependency implementation using core frameworks
1 GitHub stars
Conceptual checkpoint questions to verify understanding at every stage
Structured multi-phase implementation roadmap for research papers
Rigorous debugging gauntlet including overfit tests and gradient checks
Domain-specific tips for Transformers, RL, Generative Models, and CV

사용 사례

Reproducing a new Deep Learning architecture from an arXiv paper
Building custom model layers or loss functions without high-level wrappers
Deepening AI knowledge by implementing core algorithms from first principles