Word Embeddings Explained
🔥 Simple explanation of Word Embeddings. Words are represented as n-dimensional dense vectors. Beauty of Word Embeddings is in the way it manages to retain the semantic relationship among words, as every token has n-dimensional parameters explaining its characteristics.
Happy learning :)
🔥 Sections:
00:00 Introduction
00:29 Limitations of One-hot Encoding
02:09 Word Embeddings are powerful!!
03.26 Real-world Application
🔥 Resources:
Sentiment Analysis with Deep Neural Networks - using Word Embeddings: • Sentiment Analysis with LSTM | Deep L...
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2. Model Deployment: • PIP + Virtual Environment | Flask Dep... (Part-2)
Sentiment Analysis Project using Traditional ML: • Sentiment Analysis Project using Mach...
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