Linking pages
- 8 Creators and Core Contributors Talk About Their Model Training Libraries From PyTorch Ecosystem - neptune.ai https://neptune.ai/blog/model-training-libraries-pytorch-ecosystem 6 comments
- Marvel, Stanford & CMU NLP Playlists, Voynich, Bitter Lesson Vol. 2, ICLR 2019, Dialogue Demos | Revue http://newsletter.ruder.io/issues/marvel-stanford-cmu-nlp-playlists-voynich-bitter-lesson-vol-2-iclr-2019-dialogue-demos-173432 1 comment
- GANs, RL, and transfer learning oh my! (Practical AI #47) |> Changelog https://changelog.com/practicalai/47 0 comments
- Building a Multi-Turn Chatbot with GPT and SageMaker: A Step-by-Step Guide | by Arun Shankar | Feb, 2023 | ITNEXT https://medium.com/@shankar.arunp/building-a-multi-turn-chatbot-with-gpt-and-sagemaker-a-step-by-step-guide-7d75f33ccea1?sk=c29bdb6e51f02465e994cabea0f28ecc&source=friends_link 0 comments
Linked pages
- [1706.03762] Attention Is All You Need https://arxiv.org/abs/1706.03762 145 comments
- Better Language Models and Their Implications https://openai.com/blog/better-language-models/ 99 comments
- Hugging Face – The AI community building the future. https://huggingface.co/ 57 comments
- The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time. https://jalammar.github.io/illustrated-transformer/ 36 comments
- [1810.04805] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding https://arxiv.org/abs/1810.04805 25 comments
- ParlAI http://parl.ai/ 0 comments
- [1801.07243] Personalizing Dialogue Agents: I have a dog, do you have pets too? https://arxiv.org/abs/1801.07243 0 comments
- [1805.04833] Hierarchical Neural Story Generation https://arxiv.org/abs/1805.04833 0 comments
- Hugging Face – ConvAI https://convai.huggingface.co/ 0 comments
- GitHub - huggingface/transformers: 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. https://github.com/huggingface/pytorch-pretrained-BERT 0 comments
- GitHub - pytorch/ignite: High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently. https://github.com/pytorch/ignite 0 comments
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