Learning Word Embedding

1. 1Learn the basics of word embedding and its applications
2. 2Explore different language models for learning word embedding
3. 3Understand how loss functions are designed for word embedding models
4. 4Experiment with popular word embedding models and evaluate their performance

NLP engineers and data scientists can benefit from understanding word embedding to improve language models and text analysis tasks

💡 Word embedding can capture nuanced relationships between words, such as vector('cat') - vector('kitten') being similar to vector('dog') - vector('puppy')

📚 Word embedding represents words as numeric vectors, revealing hidden relationships between words