Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
First read
d2l-ai/d2l-en is better read as a directional signal than a clean recommendation. Keep the snapshot conservative and validate source activity, package reality, and close alternatives before committing to it.
29.4K public stars in the current GitStar snapshot.
Recognizable in the ecosystem
Last commit Aug 18, 2024.
Stale activity
Treat stars as discovery context until a linked package appears.
No linked package mapping
One or more key signals are partial, so GitStar keeps the interpretation conservative.
Partial snapshot
Snapshot facts
Compare lens
huggingface/transformers and keras-team/keras are the closest comparison targets GitStar found. A side-by-side comparison usually tells you more than a single raw rank.
Signal trail
Read the recent motion first. This block is for deciding whether the repository still looks alive, compounding, or flattening before you trust stars alone.
Package reality
No linked npm or PyPI package is mapped for this repository yet, so the page leans more heavily on GitHub-visible popularity and should be read more conservatively.
GitStar expects a package signal here, but no npm or PyPI package is linked to this repository yet.
Validation note
GitStar can summarize public signals for d2l-ai/d2l-en, but the GitHub repository is still the primary place to confirm release cadence, issue activity, and maintainer intent.
GitStar surfaces public popularity and package signals. These rankings are not endorsements, security reviews, or investment advice.
Why this rank
This repository stands out because it combines stable visibility.
Reconstructed from current stars and cached daily/weekly/monthly deltas.
GitStar can see repository momentum, but it does not have a reliable linked package signal yet.
Treat stars and recent movement as discovery context only until npm or PyPI usage is available.
Shares the learning category footprint with d2l-ai/d2l-en, so the comparison is closer to a same-problem decision than a same-language coincidence.
Shares the learning category footprint with d2l-ai/d2l-en, so the comparison is closer to a same-problem decision than a same-language coincidence.
Shares the learning category footprint with d2l-ai/d2l-en, so the comparison is closer to a same-problem decision than a same-language coincidence.
Shares the learning category footprint with d2l-ai/d2l-en, so the comparison is closer to a same-problem decision than a same-language coincidence.
GitStar picked transformers + keras as the closest next comparison from the related repository set.
[](https://gitstar.space/repo/d2l-ai/d2l-en)<a href="https://gitstar.space/repo/d2l-ai/d2l-en"><img src="https://gitstar.space/api/badge/d2l-ai/d2l-en" alt="GitStar"></a>🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
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This page provides a quick overview of d2l-ai/d2l-en based on GitStar's cached data. The signal chart reconstructs approximate checkpoints from current stars plus cached daily, weekly, and monthly star deltas, so it is best read as directional context rather than as a precise historical audit log.
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