Hacker News
- The Illustrated Transformer https://jalammar.github.io/illustrated-transformer/ 4 comments
- The Illustrated Transformer http://jalammar.github.io/illustrated-transformer/ 2 comments
- Do not understand how query, key and value matrices are generated in multi-headed self attention. https://jalammar.github.io/illustrated-transformer/ 5 comments learnmachinelearning
- ML Visualization Software Question https://jalammar.github.io/illustrated-transformer/ 4 comments deeplearning
- ML Visualization Software https://jalammar.github.io/illustrated-transformer/ 4 comments datascience
Linking pages
- imaginAIry/README.md at master · brycedrennan/imaginAIry · GitHub https://github.com/brycedrennan/imaginAIry 280 comments
- Tempering Expectations for GPT-3 and OpenAI’s API | Max Woolf's Blog https://minimaxir.com/2020/07/gpt3-expectations/ 189 comments
- AlphaFold 2 is here: what’s behind the structure prediction miracle | Oxford Protein Informatics Group https://www.blopig.com/blog/2021/07/alphafold-2-is-here-whats-behind-the-structure-prediction-miracle/ 93 comments
- Lessons Learned from two years as a Data Scientist https://dawndrain.github.io/braindrain/two_years.html 83 comments
- Building Custom Deep Learning Based OCR models https://nanonets.com/blog/attention-ocr-for-text-recogntion/ 69 comments
- The Illustrated Retrieval Transformer – Jay Alammar – Visualizing machine learning one concept at a time. http://jalammar.github.io/illustrated-retrieval-transformer/ 55 comments
- Transformers from scratch | peterbloem.nl http://peterbloem.nl/blog/transformers 40 comments
- The Illustrated Word2vec – Jay Alammar – Visualizing machine learning one concept at a time. https://jalammar.github.io/illustrated-word2vec/ 39 comments
- The fall of RNN / LSTM. We fell for Recurrent neural networks… | by Eugenio Culurciello | Towards Data Science https://towardsdatascience.com/the-fall-of-rnn-lstm-2d1594c74ce0 27 comments
- Transformers are Graph Neural Networks https://thegradient.pub/transformers-are-graph-neural-networks/ 25 comments
- Techniques for Training Large Neural Networks https://openai.com/blog/techniques-for-training-large-neural-networks/ 23 comments
- A Visual Intro to NumPy and Data Representation – Jay Alammar – Visualizing machine learning one concept at a time. https://jalammar.github.io/visual-numpy/ 22 comments
- ML Resources https://sgfin.github.io/learning-resources/ 21 comments
- Transformers for software engineers - Made of Bugs https://blog.nelhage.com/post/transformers-for-software-engineers/ 20 comments
- The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) – Jay Alammar – Visualizing machine learning one concept at a time. https://jalammar.github.io/illustrated-bert/ 20 comments
- Transformers are Graph Neural Networks | NTU Graph Deep Learning Lab https://graphdeeplearning.github.io/post/transformers-are-gnns/ 19 comments
- GitHub - amitness/learning: A log of things I'm learning https://github.com/amitness/learning 17 comments
- How Transformers Work. Transformers are a type of neural… | by Giuliano Giacaglia | Towards Data Science https://medium.com/@giacaglia/transformers-141e32e69591 17 comments
- Generating Fake News with OpenAI’s Language Models | by Adrian Yijie Xu | Towards Data Science https://towardsdatascience.com/creating-fake-news-with-openais-language-models-368e01a698a3#f7e4-509ea6e7d52d 16 comments
- Handwriting Recognition with ML (An In-Depth Guide) https://nanonets.com/blog/handwritten-character-recognition/ 15 comments
Linked pages
- https://arxiv.org/abs/1706.03762 89 comments
- Visual Information Theory -- colah's blog https://colah.github.io/posts/2015-09-Visual-Information/ 66 comments
- [1706.05137] One Model To Learn Them All https://arxiv.org/abs/1706.05137 3 comments
- The Annotated Transformer https://nlp.seas.harvard.edu/2018/04/03/attention.html 3 comments
- Transformer: A Novel Neural Network Architecture for Language Understanding – Google AI Blog https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html 3 comments
- Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention) – Jay Alammar – Visualizing machine learning one concept at a time. https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/ 1 comment
- Kullback-Leibler Divergence Explained — Count Bayesie https://www.countbayesie.com/blog/2017/5/9/kullback-leibler-divergence-explained 0 comments
- [1801.10198] Generating Wikipedia by Summarizing Long Sequences https://arxiv.org/abs/1801.10198 0 comments
- GitHub - tensorflow/tensor2tensor: Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. https://github.com/tensorflow/tensor2tensor 0 comments
- Train and run machine learning models faster | Cloud TPU | Google Cloud https://cloud.google.com/tpu/ 0 comments
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