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#bayes — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #bayes, aggregated by home.social.

  1. How a computer reads text - from counting words to vectors

    From tokenization through TF-IDF and Markov chains, to Word2Vec. How a computer turns text into numb...

    gruszka.dev/en/how-computer-re
    #llm #ai #nlp #tokenization #word2vec #embeddings #tfidf #markov #bayes #languagemodels

  2. How a computer reads text - from counting words to vectors

    From tokenization through TF-IDF and Markov chains, to Word2Vec. How a computer turns text into numb...

    gruszka.dev/en/how-computer-re
    #llm #ai #nlp #tokenization #word2vec #embeddings #tfidf #markov #bayes #languagemodels

  3. How a computer reads text - from counting words to vectors

    From tokenization through TF-IDF and Markov chains, to Word2Vec. How a computer turns text into numb...

    gruszka.dev/en/how-computer-re
    #llm #ai #nlp #tokenization #word2vec #embeddings #tfidf #markov #bayes #languagemodels

  4. How a computer reads text - from counting words to vectors

    From tokenization through TF-IDF and Markov chains, to Word2Vec. How a computer turns text into numb...

    gruszka.dev/en/how-computer-re
    #llm #ai #nlp #tokenization #word2vec #embeddings #tfidf #markov #bayes #languagemodels

  5. How a computer reads text - from counting words to vectors

    From tokenization through TF-IDF and Markov chains, to Word2Vec. How a computer turns text into numb...

    gruszka.dev/en/how-computer-re
    #llm #ai #nlp #tokenization #word2vec #embeddings #tfidf #markov #bayes #languagemodels

  6. Jak komputer czyta tekst - od liczenia słów do wektorów

    Od tokenizacji przez TF-IDF i łańcuchy Markowa, aż po Word2Vec. Jak komputer zamienia tekst w liczby...

    gruszka.dev/jak-komputer-czyta
    #llm #ai #nlp #tokenizacja #word2vec #embeddings #tfidf #markow #bayes #languagemodels

  7. Jak komputer czyta tekst - od liczenia słów do wektorów

    Od tokenizacji przez TF-IDF i łańcuchy Markowa, aż po Word2Vec. Jak komputer zamienia tekst w liczby...

    gruszka.dev/jak-komputer-czyta
    #llm #ai #nlp #tokenizacja #word2vec #embeddings #tfidf #markow #bayes #languagemodels

  8. Jak komputer czyta tekst - od liczenia słów do wektorów

    Od tokenizacji przez TF-IDF i łańcuchy Markowa, aż po Word2Vec. Jak komputer zamienia tekst w liczby...

    gruszka.dev/jak-komputer-czyta
    #llm #ai #nlp #tokenizacja #word2vec #embeddings #tfidf #markow #bayes #languagemodels

  9. Jak komputer czyta tekst - od liczenia słów do wektorów

    Od tokenizacji przez TF-IDF i łańcuchy Markowa, aż po Word2Vec. Jak komputer zamienia tekst w liczby...

    gruszka.dev/jak-komputer-czyta
    #llm #ai #nlp #tokenizacja #word2vec #embeddings #tfidf #markow #bayes #languagemodels

  10. Jak komputer czyta tekst - od liczenia słów do wektorów

    Od tokenizacji przez TF-IDF i łańcuchy Markowa, aż po Word2Vec. Jak komputer zamienia tekst w liczby...

    gruszka.dev/jak-komputer-czyta
    #llm #ai #nlp #tokenizacja #word2vec #embeddings #tfidf #markow #bayes #languagemodels

  11. Cell migration is fundamental to many biological processes (cancer metastasis, cellular immunity, development, ...). Here we introduce a new computational method & free tool to evaluate high-throughput cell migration assays.
    #cancer #cellmigration #Bayes #quantitativebiology
    doi.org/10.1371/journal.pcbi.1

  12. Cell migration is fundamental to many biological processes (cancer metastasis, cellular immunity, development, ...). Here we introduce a new computational method & free tool to evaluate high-throughput cell migration assays.
    #cancer #cellmigration #Bayes #quantitativebiology
    doi.org/10.1371/journal.pcbi.1

  13. Cell migration is fundamental to many biological processes (cancer metastasis, cellular immunity, development, ...). Here we introduce a new computational method & free tool to evaluate high-throughput cell migration assays.
    #cancer #cellmigration #Bayes #quantitativebiology
    doi.org/10.1371/journal.pcbi.1

  14. Cell migration is fundamental to many biological processes (cancer metastasis, cellular immunity, development, ...). Here we introduce a new computational method & free tool to evaluate high-throughput cell migration assays.
    #cancer #cellmigration #Bayes #quantitativebiology
    doi.org/10.1371/journal.pcbi.1

  15. Cell migration is fundamental to many biological processes (cancer metastasis, cellular immunity, development, ...). Here we introduce a new computational method & free tool to evaluate high-throughput cell migration assays.
    #cancer #cellmigration #Bayes #quantitativebiology
    doi.org/10.1371/journal.pcbi.1

  16. How a computer reads text - from counting words to vectors

    From tokenization through TF-IDF and Markov chains, to Word2Vec. How a computer turns text into numb...

    gruszka.dev/en/how-computer-re
    #llm #ai #nlp #tokenization #word2vec #embeddings #tfidf #markov #bayes #languagemodels

  17. How a computer reads text - from counting words to vectors

    From tokenization through TF-IDF and Markov chains, to Word2Vec. How a computer turns text into numb...

    gruszka.dev/en/how-computer-re
    #llm #ai #nlp #tokenization #word2vec #embeddings #tfidf #markov #bayes #languagemodels

  18. How a computer reads text - from counting words to vectors

    From tokenization through TF-IDF and Markov chains, to Word2Vec. How a computer turns text into numb...

    gruszka.dev/en/how-computer-re
    #llm #ai #nlp #tokenization #word2vec #embeddings #tfidf #markov #bayes #languagemodels

  19. Bayesian priors aren't just arbitrary guesses - you can (and should) validate them. Our paper shows how to use prior predictive checks to map your domain knowledge onto the model, ensuring your assumptions generate realistic, well-calibrated priors. #Rstats #Stan #Bayes

  20. Bayesian priors aren't just arbitrary guesses - you can (and should) validate them. Our paper shows how to use prior predictive checks to map your domain knowledge onto the model, ensuring your assumptions generate realistic, well-calibrated priors. #Rstats #Stan #Bayes

  21. Alright, future engineers!
    **Conditional Probability:** P(A|B) is the prob of event A, given event B has already happened.
    Ex: P(A|B) = P(A & B) / P(B). Think: Prob of engine failure *given* low oil pressure.
    Pro-Tip: Essential for Bayesian inference & diagnostics!
    #Probability #Bayes #STEM #StudyNotes

  22. Jak komputer czyta tekst - od liczenia słów do wektorów

    Od tokenizacji przez TF-IDF i łańcuchy Markowa, aż po Word2Vec. Jak komputer zamienia tekst w liczby...

    gruszka.dev/jak-komputer-czyta
    #llm #ai #nlp #tokenizacja #word2vec #embeddings #tfidf #markow #bayes #languagemodels

  23. Jak komputer czyta tekst - od liczenia słów do wektorów

    Od tokenizacji przez TF-IDF i łańcuchy Markowa, aż po Word2Vec. Jak komputer zamienia tekst w liczby...

    gruszka.dev/jak-komputer-czyta
    #llm #ai #nlp #tokenizacja #word2vec #embeddings #tfidf #markow #bayes #languagemodels

  24. Jak komputer czyta tekst - od liczenia słów do wektorów

    Od tokenizacji przez TF-IDF i łańcuchy Markowa, aż po Word2Vec. Jak komputer zamienia tekst w liczby...

    gruszka.dev/jak-komputer-czyta
    #llm #ai #nlp #tokenizacja #word2vec #embeddings #tfidf #markow #bayes #languagemodels

  25. Jak komputer czyta tekst - od liczenia słów do wektorów

    Od tokenizacji przez TF-IDF i łańcuchy Markowa, aż po Word2Vec. Jak komputer zamienia tekst w liczby...

    gruszka.dev/jak-komputer-czyta
    #llm #ai #nlp #tokenizacja #word2vec #embeddings #tfidf #markow #bayes #languagemodels

  26. Jak komputer czyta tekst - od liczenia słów do wektorów

    Od tokenizacji przez TF-IDF i łańcuchy Markowa, aż po Word2Vec. Jak komputer zamienia tekst w liczby...

    gruszka.dev/jak-komputer-czyta
    #llm #ai #nlp #tokenizacja #word2vec #embeddings #tfidf #markow #bayes #languagemodels

  27. I like nonconformists, except in my chains. WTF is happening to the gold one?! #bayes #MCMC

  28. I like nonconformists, except in my chains. WTF is happening to the gold one?! #bayes #MCMC

  29. Hehehehe, we got another reviewer confused by our use of a 89% credible interval.
    Cue the beauty of prime numbers! And it is my co-author's birth year, I am so happy that I can put this in the answer 😅!

    #bayesian #academicchatter #bayes @rlmcelreath

  30. Hehehehe, we got another reviewer confused by our use of a 89% credible interval.
    Cue the beauty of prime numbers! And it is my co-author's birth year, I am so happy that I can put this in the answer 😅!

    #bayesian #academicchatter #bayes @rlmcelreath

  31. Returning to Bayesian computation now after some time away, I was delighted to see active work on JAGS 5.0!
    sourceforge.net/projects/mcmc-
    #Bayes #JAGS

  32. Returning to Bayesian computation now after some time away, I was delighted to see active work on JAGS 5.0!
    sourceforge.net/projects/mcmc-
    #Bayes #JAGS

  33. 🤔 Ah, yet another "innovative" tool promising to fix your #non-deterministic #bugs by throwing #Bayes at #Git like it's some kind of magic wand. 🔮 Because clearly, what we all need in our #debugging toolbox is more statistical hand-waving and fewer #practical #solutions. 😂
    github.com/hauntsaninja/git_ba #innovative #tools #HackerNews #ngated

  34. 🤔 Ah, yet another "innovative" tool promising to fix your #non-deterministic #bugs by throwing #Bayes at #Git like it's some kind of magic wand. 🔮 Because clearly, what we all need in our #debugging toolbox is more statistical hand-waving and fewer #practical #solutions. 😂
    github.com/hauntsaninja/git_ba #innovative #tools #HackerNews #ngated

  35. You're probably familiar with git bisect, which lets you find a commit that introduces a change in behavior via binary search (`git bisect`). But what if the change in behavior is non-deterministic?

    `git bayesect` is a generalization of git bisect that uses Bayesian inference to solve this problem. If your code has started gaslighting you, give it a try!

    hauntsaninja.github.io/git_bay

  36. You're probably familiar with git bisect, which lets you find a commit that introduces a change in behavior via binary search (`git bisect`). But what if the change in behavior is non-deterministic?

    `git bayesect` is a generalization of git bisect that uses Bayesian inference to solve this problem. If your code has started gaslighting you, give it a try!

    hauntsaninja.github.io/git_bay #git #bayes

  37. 🖤💙 Oh, how nice! The International Labour Organization provided the recording of my yesterday's seminar on my new forecasting system for labour market outcomes and my R package bpvars I developed for them! It's all very good 🤍

    youtube.com/watch?v=ef3eXbqNbr8

  38. 🖤💙 Oh, how nice! The International Labour Organization provided the recording of my yesterday's seminar on my new forecasting system for labour market outcomes and my R package bpvars I developed for them! It's all very good 🤍

    youtube.com/watch?v=ef3eXbqNbr8

    #forecast #labourmarkets #Bayes #VAR #rstats #bpvars

  39. 🖤💙 Oh, how nice! The International Labour Organization provided the recording of my yesterday's seminar on my new forecasting system for labour market outcomes and my R package bpvars I developed for them! It's all very good 🤍

    youtube.com/watch?v=ef3eXbqNbr8

    #forecast #labourmarkets #Bayes #VAR #rstats #bpvars

  40. 🖤💙 Oh, how nice! The International Labour Organization provided the recording of my yesterday's seminar on my new forecasting system for labour market outcomes and my R package bpvars I developed for them! It's all very good 🤍

    youtube.com/watch?v=ef3eXbqNbr8

    #forecast #labourmarkets #Bayes #VAR #rstats #bpvars

  41. My friend Jay Wren recommended a book yesterday at lunch:

    "Thinking, Fast and Slow"
    by Daniel Kahneman

    amazon.com/Thinking-Fast-Slow-

    A recommendation from Jay is an insta-buy. I got it as an audio-book (because of my commute).

    It is not **at all** what I was expecting! I guess I thought maybe I was expecting something like a "self-help" book or the like. No. This is **not** a book aimed at a broad audience. This is a book aimed at people who understand (at least a bit about) probability and bias and category theory and ... What I'm saying is: it's not fluff. It's genuine knowledge aimed square at me. Jay's recommendation was on the money.

    I wouldn't hand this to my MIL (it's past her at this point). I wouldn't hand it to my wife (she's certainly smart enough, but I don't think it falls in her circle of interest). I absolutely **would** recommend it to **you**, or to anyone in my circle of friends.

    Go have some fun!

    #Bayes #Knowledge #Psychology #Thinking