Summary of Word2vec: Learning Word Embeddings
Learn to understand the principles behind Word2vec and classical word representation methods. Explore how neural network models like skip-gram and CBOW generate word embeddings and implement these approaches using TensorFlow for natural language tasks. Gain foundational skills to apply rich word embeddings in various NLP applications.
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Word embeddings have become an integral part of many NLP tasks and are widely used for tasks such as machine translation, chatbots, image caption generation, and language modeling. Not only do word embeddings act as a dimensionality reduction technique (compared to one-hot encoding) but they also give a richer feature representation than other ...