Uploaded December 2025 | Updated September 2026, 2 weeks ago

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In part 3, we explore how vector embeddings work under the hood, starting with Word2Vec, one of the first models to encode word meanings as vectors.

In this section, we're going to go over:

- How Word2Vec converts words into vectors using neural networks
- Training on large text corpora to learn word associations
- The skip-gram approach: predicting surrounding words
- Challenges like multiple word meanings and sentence embeddings
- How the model improves through error correction during training

Next, we’ll explore more advanced embedding models and applications beyond words.

#embeddings #nlp #machinelearning #deeplearning #ai #neuralnetworks #vectorembeddings #languagemodels #textanalysis #mlbasics #nlptechniques

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