Active Deep Learning work spans four areas: neural network architectures, distributed training infrastructure, optimization techniques, and model deployment. Below is a curated list of Deep Learning experts driving those areas.
| Avatar | Name | GitHub | Notable Contributions† |
|---|---|---|---|
 |
scutan90 | @scutan90 | Maintains DeepLearning-500-questions |
 |
Lutz Roeder | @lutzroeder | netron contributor |
 |
Flood Sung | @floodsung | Maintains Deep-Learning-Papers-Reading-Roadmap |
 |
Ashish Patel | @ashishpatel26 | Maintains 500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code |
 |
Yunjey Choi | @yunjey | Maintains pytorch-tutorial |
 |
Erik Linder-Norén | @eriklindernoren | Maintains ML-From-Scratch |
 |
Donne Martin | @donnemartin | Maintains data-science-ipython-notebooks |
 |
François Chollet | @fchollet | keras contributor |
 |
Aston Zhang | @astonzhang | d2l-en contributor |
 |
Eren Gölge | @erogol | TTS contributor |
 |
Evan Shelhamer | @shelhamer | caffe contributor |
 |
Jeff Donahue | @jeffdonahue | caffe contributor |
 |
Ming Xu (徐明) | @shibing624 | Deep Learning developer and practitioner |
 |
Somshubra Majumdar | @titu1994 | Interested in Machine Learning, Deep Learning and Data Science in general |
† This is not a ranking — the order doesn't reflect quality or experience.
Together, these Deep Learning experts shape the project's roadmap and surrounding ecosystem.
AI & Machine Learning: LangChain · OpenCV · Claude · NLP · LlamaIndex · LangGraph
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