It’s all about using our technology and research to help enrich people’s lives. Like YouTube — and its mission to give everyone a voice and show them the world.Read More
YouTube: Enhancing the user experience
It’s all about using our technology and research to help enrich people’s lives. Like YouTube — and its mission to give everyone a voice and show them the world.Read More
YouTube: Enhancing the user experience
It’s all about using our technology and research to help enrich people’s lives. Like YouTube — and its mission to give everyone a voice and show them the world.Read More
YouTube: Enhancing the user experience
It’s all about using our technology and research to help enrich people’s lives. Like YouTube — and its mission to give everyone a voice and show them the world.Read More
YouTube: Enhancing the user experience
It’s all about using our technology and research to help enrich people’s lives. Like YouTube — and its mission to give everyone a voice and show them the world.Read More
YouTube: Enhancing the user experience
It’s all about using our technology and research to help enrich people’s lives. Like YouTube — and its mission to give everyone a voice and show them the world.Read More
YouTube: Enhancing the user experience
It’s all about using our technology and research to help enrich people’s lives. Like YouTube — and its mission to give everyone a voice and show them the world.Read More
🎉 PyTorch Docathon H1 2023 Wrap-up 🎉
Thank you to all who participated in our first ever PyTorch Docathon, the results have been nothing short of amazing! We want to extend our sincerest gratitude to all the participants who made this event a resounding success. Your passion, talent, and hard work have left an indelible mark on the PyTorch documentation.
The virtual Docathon ran from May 31 through June 15 with more than 230 registrants and more than 110 participants joining the Docathon Slack channel, the energy and enthusiasm were palpable. Entrants were judged on the difficulty of submissions that resulted in over 40 merged pull requests and the publication of four new tutorials and addition of one new example.
We want to give a special shout-out to our top contributors, who went above and beyond during this event. Your dedication and expertise have been invaluable in enhancing the PyTorch documentation and empowering developers worldwide. See the full list of contributors here.
Meet the top contributors:
- First place: JoseLuisC99, QasimKhan5x, bjhargrave
- Second place: Aidyn-A, CaoE, HemanthSai7, leslie-fang-intel, Valentine233
- Third place: TheMemoryDealer, arunppsg, noqqaqq, zabboud, kiersten-stokes
- Honorable mentions: frasertajima, nairbv, mikebrow, NeoKish, fabiogomez11c
As we bring this Docathon to a close, we encourage each and every one of you to stay inspired and keep contributing to PyTorch documentation and code, and pushing the boundaries of what’s possible with PyTorch. Your collective efforts are shaping the landscape of deep learning and fostering innovation in the AI community.
Team PyTorch
Symphony: Composing Interactive Interfaces for Machine Learning
Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, recent studies of ML teams and our interviews with practitioners (n=9) showed that ML interfaces have limited adoption in practice. While existing ML interfaces are effective for specific tasks, they are not designed to be reused, explored, and shared by multiple stakeholders in cross-functional teams. To enable analysis and communication between different ML practitioners, we designed and implemented Symphony, a…Apple Machine Learning Research
Cross-lingual Knowledge Transfer and Iterative Pseudo-labeling for Low-Resource Speech Recognition with Transducers
Voice technology has become ubiquitous recently. However, the accuracy, and hence experience, in different languages varies significantly, which makes the technology not equally inclusive. The availability of data for different languages is one of the key factors affecting accuracy, especially in training of all-neural end-to-end automatic speech recognition systems.
Cross-lingual knowledge transfer and iterative pseudo-labeling are two techniques that have been shown to be successful for improving the accuracy of ASR systems, in particular for low-resource languages, like Ukrainian.
Our…Apple Machine Learning Research