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Dev Anand on Instagram: "Tokenization is important because AI models operate on numerical representations, not raw text. To convert language into numbers efficiently, text must be broken into meaningful, reusable units. Simply breaking text into words is not true tokenization because words are too many and too variable. A word-level approach treats every variation as a new symbol, which leads to an unnecessarily large vocabulary, poor generalization, and inefficient learning. Tokenization instea
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Volkan Erdogan | Software Engineer on Instagram: "Comment “NLP” for the links! You Will Never Struggle With NLP (Natural Language Processing) Again 🤖 📌 Watch these beginner-friendly videos: 1️⃣ What is NLP (Natural Language Processing)? – by IBM Technology 2️⃣ NLP Tutorial for Beginners in Python – full playlist by Codebasics 3️⃣ Natural Language Processing with spaCy & Python – course for beginners by freeCodeCamp Stop feeling lost with tokenization, sentiment analysis, and text
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