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7 related articles
·4 min
Deep Dive into Word Embeddings: The Semantic Foundation of Large Language Models
A detailed explanation of word embedding principles, from one-hot encoding to contextual embeddings, covering embedding matrices, positional encoding, and RAG applications for LLM developers.
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·4 min
Word Embeddings Explained: The Logical Relationship Between Word2Vec, SVD, and GloVe
A detailed breakdown of how Word2Vec, SVD, and GloVe relate: Word2Vec uses prediction, co-occurrence matrix + SVD uses counting, and GloVe merges both approaches into a unified word embedding framework.
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·4 min
Word Embedding Learning Roadmap: A Complete Guide from word2vec to Transformer Embeddings
A systematic learning path for NLP beginners covering word2vec principles and implementation, GloVe comparison, Transformer contextual embeddings, required math foundations, and recommended resources.
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·3 min
Why Can't Word2vec Handle Out-of-Vocabulary Words? OOV Problem Explained with Solutions
Deep dive into why Word2vec can't handle OOV words, explaining the lookup table mechanism and covering solutions like FastText subwords, UNK tokens, and BERT.
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·3 min
From Word Vectors to Large Language Models: The Complete Evolution of AI 2.0 Technology
From word vectors and embeddings to RNNs, BERT, Transformers, and ChatGPT — a complete guide to the technical evolution of large language models and the AI 2.0 era.
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·4 min
No MLP or Attention Required: How Point-Attractor Dynamics Learn Word Embeddings
A minimalist dynamical system experiment: without MLP, Transformer, or attention layers, point-attractor dynamics driven purely by co-occurrence pressure learns semantic similarity on SimLex-999.
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