Links to resources, books, tutorials and other stuffs that are useful technically.

• Rajkomar, A., Dean, J. and Kohane, I., 2019. Machine learning in medicine. New England Journal of Medicine, 380(14), pp.1347-1358. Paper link
• Yoshua Bengio, Yann Lecun, Geoffrey Hinton. Deep Learning for AI Communications of the ACM, July 2021, Vol. 64 No. 7, Pages 58-65. https://cacm.acm.org/magazines/2021/7/253464-deep-learning-for-ai/fulltext

• Our own bi-weekly group seminars: https://github.com/knowlab/bi-weekly-paper-presentation
• Usher Institute Events (various seminars): https://www.ed.ac.uk/usher/news-events/events
• JAMIA Journal Club: https://www.amia.org/category/webinars/journal-club

• (Manning et al, 2015) Advances in natural language processing - https://science.sciencemag.org/content/349/6245/261.full
• Ghahramani, Z., 2015. Probabilistic machine learning and artificial intelligence. Nature, 521(7553), pp.452-459. https://www.nature.com/articles/nature14541

• Practical NLP with Spacy, NLTK, and Stanza

  • https://realpython.com/natural-language-processing-spacy-python/
  • https://spacy.io/usage/spacy-101
  • http://www.nltk.org/book/
  • https://stanfordnlp.github.io/stanza/biomed.html (Biomedical Models in Stanza)

• NLP with machine learning

  • https://www.coursera.org/specializations/natural-language-processing
  • https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z

• Curated embedding models

  • https://github.com/Separius/awesome-sentence-embedding

• (Michael Galkin) Knowledge Graph in NLP - ACL2020: https://towardsdatascience.com/knowledge-graphs-in-natural-language-processing-acl-2020-ebb1f0a6e0b1

• (venturebeat) Why knowledge graphs are key to working with data efficiently, powerfully: https://venturebeat.com/2021/06/28/why-knowledge-graphs-are-key-to-working-with-data-efficiently-powerfully/
• (Our own KG book) Exploiting Linked Data and Knowledge Graphs in Large Organisations: https://link.springer.com/book/10.1007%2F978-3-319-45654-6
• Ghahramani, Z., 2015. Probabilistic machine learning and artificial intelligence. Nature, 521(7553), pp.452-459. https://www.nature.com/articles/nature14541

• Farewell RNNs, Welcome TCNs: https://towardsdatascience.com/farewell-rnns-welcome-tcns-dd76674707c8
• A repo with essential readings for GNN: https://github.com/thunlp/GNNPapers

• Lastest papers on automated medical coding from free-text clinical notes: https://github.com/acadTags/medical-coding-NLP

• An ongoing curation of COVID-related NLP tools: https://github.com/acadTags/Awesome-COVID-NLP-tools