Train a New Language Model Using Transformers and Tokenizers. https://huggingface.co/blog/how-to-train

https://johnbrandt.org/blog/sentence-similarity/

Stanford Speech and Language processing :- https://web.stanford.edu/~jurafsky/slp3/?fbclid=IwAR2E5UzuV8TjXOtooTZhwo4MuJVwFdrTQHRPXcIFejgKoI5ewyZEMxEShvM

Kaldi the speech Recognition Pretrained Module :-https://kaldi-asr.org/models.html?fbclid=IwAR1eepp7pxKB5hSGeQnpUeUu0K9ZwGGsbg2_abOYlQU7Uu6-IgkM_D9lUb0

Language Models implemented by NVIDIA.
1) http://bit.ly/379dhO8
2) http://bit.ly/2Rwd69b
3) http://bit.ly/2NDefKJ

Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs

https://medium.com/tensorflow/deeppavlov-an-open-source-library-for-end-to-end-dialog-systems-and-chatbots-31cf26849e37 A question and Answering system to make response of Customers easy.

Sequence-to-Sequence in NLP :-
1) https://towardsdatascience.com/word-sequence-decoding-in-seq2seq-architectures-d102000344ad

Some of the Speech Recognition tools :-
1) http://kaldi-asr.org/
2) https://speechbrain.github.io/?fbclid=IwAR1R9wC2sVE-TNPFaL-Dp6PpZHVqN_-nwpzxfCM2jh9mXkPb88-GZJT9pnM
3) https://www.youtube.com/watch?v=BNCgE3cZC3A

Machine Learning behind Google Recoder App :- https://analyticsindiamag.com/machine-learning-google-recorder-pixel-mobile-ai/

https://medium.com/s/story/spotifys-discover-weekly-how-machine-learning-finds-your-new-music-19a41ab76efe

How to solve 90 % of NLP problem https://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e
Time series forcasting :- https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/

1) https://ai.googleblog.com/2019/12/the-on-device-machine-learning-behind.html
2) https://facebookresearch.github.io/vizseq/
3) https://arxiv.org/pdf/1909.05424.pdf
4) https://ai.facebook.com/blog/vizseq-a-visual-analysis-toolkit-for-accelerating-text-generation-research/

https://towardsdatascience.com/a-list-of-beginner-friendly-nlp-projects-using-pre-trained-models-dc4768b4bec0