Linking pages
- Using Transfer Learning and Pre-trained Language Models to Classify Spam | by Steve Mutuvi | Heartbeat https://heartbeat.fritz.ai/using-transfer-learning-and-pre-trained-language-models-to-classify-spam-549fc0f56c20 0 comments
- FastAI Sentiment Analysis. Analyze the sentiment of tweets using… | by Gilbert Tanner | Heartbeat https://heartbeat.fritz.ai/fastai-sentiment-analysis-bee61ca7835d 0 comments
- Comparing MobileNet Models in TensorFlow | by Harshit Dwivedi | Heartbeat https://heartbeat.fritz.ai/exploring-the-mobilenet-models-in-tensorflow-d9d21774cdab 0 comments
Linked pages
- The Unreasonable Effectiveness of Recurrent Neural Networks https://karpathy.github.io/2015/05/21/rnn-effectiveness/ 434 comments
- Transitioning entirely to neural machine translation - Engineering at Meta https://code.facebook.com/posts/289921871474277/transitioning-entirely-to-neural-machine-translation/ 242 comments
- [1609.08144] Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation http://arxiv.org/abs/1609.08144 97 comments
- [1511.06388] sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings http://arxiv.org/abs/1511.06388 41 comments
- Stanford CS 224N | Natural Language Processing with Deep Learning https://web.stanford.edu/class/cs224n/ 28 comments
- Medium https://medium.com/m/signin?isDraft=1&operation=login&redirect=https%3A%2F%2Fmedium.com%2F%40jamie_34747%2F79d382edf22b%3Fsource%3D 19 comments
- GloVe: Global Vectors for Word Representation http://nlp.stanford.edu/projects/glove/ 7 comments
- Comet ML - Build better models faster https://www.comet.ml/ 4 comments
- [1705.08039] Poincaré Embeddings for Learning Hierarchical Representations https://arxiv.org/abs/1705.08039 0 comments
- [1506.06726] Skip-Thought Vectors http://arxiv.org/abs/1506.06726 0 comments
- [1607.04606] Enriching Word Vectors with Subword Information https://arxiv.org/abs/1607.04606 0 comments
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