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transformers and attention lectures adde

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Navaneeth Malingan 2021-02-13 16:43:14 +05:30
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@ -351,12 +351,20 @@ _Note: Below you can find the best lectures for popular Machine Learning Algorit
- [LSTM implementation explained](https://apaszke.github.io/lstm-explained.html)
- [A Gentle Introduction to LSTM Autoencoders](https://machinelearningmastery.com/lstm-autoencoders/)
- [Keras LSTM tutorial – How to easily build a powerful deep learning language model](http://adventuresinmachinelearning.com/keras-lstm-tutorial/)
### Transformers and Self Attention
#### Visual Guide to Transformer Neural Networks (Highly Recommended)
- [Part 1 - Position Embeddings](https://www.youtube.com/watch?v=dichIcUZfOw&ab_channel=Hedu-MathofIntelligence)
- [Part 2 - Multi-Head & Self Attention](https://www.youtube.com/watch?v=mMa2PmYJlCo&ab_channel=Hedu-MathofIntelligence)
- [Part 3 - Decoder’s Masked Attention](https://www.youtube.com/watch?v=gJ9kaJsE78k&ab_channel=Hedu-MathofIntelligence)
- [NLP Transformers Attention Playlist](https://www.youtube.com/playlist?list=PLxI06TjqRkofnRuvD-Ks0Qef5g73yrMQ7)
- [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)
- [The Annotated Transformer](https://nlp.seas.harvard.edu/2018/04/03/attention.html)
### BERT
- [A Visual Guide to Using BERT for the First Time](https://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/)
- [The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)](https://jalammar.github.io/illustrated-bert/)
- [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/)
- [The Annotated Transformer](https://nlp.seas.harvard.edu/2018/04/03/attention.html)
- [BERT Explained: State of the art language model for NLP](https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270)
- [BERT – State of the Art Language Model for NLP](https://www.lyrn.ai/2018/11/07/explained-bert-state-of-the-art-language-model-for-nlp/)
- [BioBERT, a language representation model for biomedical domain, especially designed for biomedical text mining tasks such as biomedical named entity recognition, relation extraction, question answering, etc](https://github.com/dmis-lab/biobert)