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Polski(PL) 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...

LLM text processing explained: from word counts to linguistics and semiotics · 8 sources tracked

A series of articles explores the technical underpinnings of how Large Language Models (LLMs) process and understand text. The author delves into various methods, from basic word counting and statistical techniques like TF-IDF and Markov chains to more advanced neural network approaches such as Word2Vec, MLPs, RNNs, and LSTMs. The series also touches upon the linguistic features and semiotic theories essential for comprehending LLM functionality, emphasizing that these models are sophisticated machines rather than conscious minds. AI

IMPACT Provides foundational knowledge on LLM text processing, linguistics, and semiotics for AI practitioners.

RANK_REASON The cluster consists of blog posts explaining technical concepts related to LLMs, rather than a primary release or significant industry event.

Read on Mastodon — mastodon.social →

AI-generated summary · Google Gemini · from 8 sources. How we write summaries →

LLM text processing explained: from word counts to linguistics and semiotics · 8 sources tracked

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The cluster consists of blog posts explaining technical concepts related to LLMs, rather than a primary release or significant industry event.
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8 independent sources
Strong cross-source corroboration — multiple independent publishers covered this within the clustering window.
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paper, other
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High
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47 days old
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COVERAGE [8]

  1. Mastodon — mastodon.social TIER_1 English(EN) · blazejgruszka ·

    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.

    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... https:// gruszka.dev/en/how-computer-re ads-text.html # llm # ai # nlp # tokenization # word2vec # embeddings # tfidf…

  2. Mastodon — mastodon.social TIER_1 English(EN) · blazejgruszka ·

    Linguistic features - what you need to know before you understand how an LLM thinks Five layers of language - phonetics, morphology, syntax, semantics, pragmati

    Linguistic features - what you need to know before you understand how an LLM thinks Five layers of language - phonetics, morphology, syntax, semantics, pragmatics - and how an LLM hand... https:// gruszka.dev/en/linguistic-feat ures-and-llm.html # llm # ai # nlp # linguistics # l…

  3. Mastodon — mastodon.social TIER_1 English(EN) · blazejgruszka ·

    From simple neurons to memory - the evolution of language models From a single neuron in 1958, through MLP and RNN with the forgetting problem, to LSTM with mem

    From simple neurons to memory - the evolution of language models From a single neuron in 1958, through MLP and RNN with the forgetting problem, to LSTM with memory g... https:// gruszka.dev/en/from-neurons-to -memory.html # llm # ai # neuralnetworks # rnn # lstm # perceptron # ml…

  4. Mastodon — mastodon.social TIER_1 English(EN) · blazejgruszka ·

    Semiotics - why an LLM doesn't Saussure, Peirce and Derrida as the key to understanding LLMs. Why a model is not a mind, but a mach... https:// gruszka.dev/en/s

    Semiotics - why an LLM doesn't Saussure, Peirce and Derrida as the key to understanding LLMs. Why a model is not a mind, but a mach... https:// gruszka.dev/en/semiotics-and-l lm.html # llm # ai # semiotics # signs # linguistics # languagemodels # saussure # peirce # derrida

  5. Mastodon — mastodon.social TIER_1 Polski(PL) · blazejgruszka ·

    From simple neurons to memory – the evolution of language models From a single neuron in 1958, through MLP and RNN with the forgetting problem, to LSTM with gates

    Od prostych neuronów do pamięci – ewolucja modeli językowych Od pojedynczego neuronu w 1958 roku, przez MLP i RNN z problemem zapominania, aż po LSTM z bramkami ... https:// gruszka.dev/od-prostych-neuron ow-do-pamieci.html # llm # ai # neuralnetworks # rnn # lstm # perceptron # …

  6. Mastodon — mastodon.social TIER_1 Polski(PL) · blazejgruszka ·

    Linguistic Features - What You Need to Know Before Understanding How LLM Thinks Five Layers of Language – Phonetics, Morphology, Syntax, Semantics, Pragmatics – and How LLMs Handle Them

    Cechy językowe - co musisz wiedzieć, zanim zrozumiesz, jak myśli LLM Pięć warstw języka – fonetyka, morfologia, składnia, semantyka, pragmatyka – i jak LLM radzi sobie z... https:// gruszka.dev/cechy-jezykowe-a-l lm.html # llm # ai # jezykoznawstwo # nlp # linguistics # languagem…

  7. Mastodon — mastodon.social TIER_1 Polski(PL) · blazejgruszka ·

    Semiotics - why LLMs are not Saussure, Peirce, and Derrida as the key to understanding LLMs. Why a model is not a mind, but a machine with... https:// gruszka.dev/semiot

    Semiotyka - dlaczego LLM nie Saussure, Peirce i Derrida jako klucz do zrozumienia LLM. Dlaczego model to nie umysł, ale maszyna z... https:// gruszka.dev/semiotyka-a-llm.ht ml # llm # ai # semiotyka # znaki # linguistics # languagemodels # saussure # peirce # derrida

  8. Mastodon — mastodon.social TIER_1 Polski(PL) · blazejgruszka ·

    How a computer reads text - from word counting to vectors From tokenization through TF-IDF and Markov chains, to Word2Vec. How a computer turns text into numbers...

    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... https:// gruszka.dev/jak-komputer-czyta -tekst.html # llm # ai # nlp # tokenizacja # word2vec # embeddings # tfidf # mar…