Uploaded December 2025 | Updated September 2026, 2 weeks ago

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In part 4, we explore how vector embeddings evolved from Word2Vec to modern Transformer models, solving key limitations of earlier approaches.
In this section, we're going to go over:

-Problems with word-level embeddings and handling multiple word meanings -The 2017 Attention is All You Need paper and the foundation of Transformer models
-How Transformers consider the entire input text for better embeddings
-Modern embedding models like BERT and ELMo Trade-offs, including increased computational requirements
-Transformers have become the foundation for modern machine learning and embedding models, enabling more accurate and context-aware vector representations.

#transformers #embeddings #nlp #deeplearning #ai #elmo #languagemodels #machinelearning #neuralnetworks #mlbasics #nlptechniques

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