tl;dr: This lecture covers essential techniques for representing words as vectors, from traditional count-based methods to advanced embedding techniques such as Word2vec.

🎓 Lecturer: Tanmoy Chakraborty [https://tanmoychak.com]
🔗 Get the Slides Here: http://lcs2.in/llm2401
📚 Suggested Readings: Chapter-6, Speech and Language Processing [https://web.stanford.edu/~jurafsky/sl...]

In this Lecture you can learn about various methods to represent words as vectors, including count-based techniques and the cutting-edge methodologies for learning embeddings like Word2vec. This session is crucial for anyone interested in the mechanics of natural language processing and how machines understand language.