deep-learning-papers-guide
Annotated deep learning paper implementations with code walkthroughs
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill deep-learning-papers-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Deep Learning Papers Guide ## Overview Understanding deep learning architectures requires more than reading papers -- it requires reading and writing code. The annotated_deep_learning_paper_implementations repository (65,800+ stars) provides line-by-line annotated implementations of seminal deep learning papers in PyTorch, making it one of the most valuable learning resources in the field. This guide organizes the key architectures by category, provides implementation patterns for the most important building blocks, and offers strategies for going from paper to working code. Whether you are implementing a Transformer variant for your research, understanding a GAN architecture for your experiments, or teaching a deep learning course, these patterns accelerate the process. The focus is on practical understanding: what each component does, why it is designed that way, and how to implement it correctly in PyTorch. ## Core Architecture Families ### Transformer Architectures The Transformer (Vaswani et al., 2017) is the foundation of modern NLP and increasingly of computer vision. #### Multi-Head Self-Attention ```python import torch import torch.nn as nn import math class MultiHeadAtt
- Overview
- Core Architecture Families
- Transformer Architectures
- Convolutional Neural Networks
- Key Architecture Comparison
- Training Patterns
- Standard Training Loop
- Learning Rate Scheduling
- From Paper to Code: A Methodology
- Best Practices
- References
What does the deep-learning-papers-guide skill do?
Annotated deep learning paper implementations with code walkthroughs
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill deep-learning-papers-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.