Language Models Bootcamp Day 1 Highlights
Vloลพit
- ฤas pลidรกn 1. 08. 2024
- ๐๐๐๐ซ๐๐ญ๐ฌ ๐จ๐ ๐๐๐ซ๐ ๐ ๐๐๐ง๐ ๐ฎ๐๐ ๐ ๐๐จ๐๐๐ฅ๐ฌ ๐๐จ๐จ๐ญ๐๐๐ฆ๐ฉ! ๐
Day 1 of our LLM Bootcamp was an exhilarating journey into the heart of Large Language Models. Here's a glimpse into the cutting-edge topics our participants dived into:
๐. ๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐๐ข๐ง๐ ๐ญ๐ก๐ ๐๐๐ ๐๐๐จ๐ฌ๐ฒ๐ฌ๐ญ๐๐ฆ: Raja Iqbal kickstarted the day with an expansive overview of the LLM ecosystem, unveiling the technologies and frameworks driving the generative AI revolution.
๐. ๐๐ก๐๐ฅ๐ฅ๐๐ง๐ ๐๐ฌ ๐๐ง๐ ๐๐ข๐ฌ๐ค๐ฌ ๐ข๐ง ๐๐ง๐ญ๐๐ซ๐ฉ๐ซ๐ข๐ฌ๐ ๐๐๐จ๐ฉ๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐๐๐ฌ: This session focused on the practical hurdles enterprises face when adopting LLMs, discussing engineering, ethical, and legal challenges. Raja equipped attendees with the knowledge to navigate and mitigate these risks effectively.
๐. ๐๐ญ๐ญ๐๐ง๐ญ๐ข๐จ๐ง ๐๐๐๐ก๐๐ง๐ข๐ฌ๐ฆ ๐๐ง๐ ๐๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ซ๐ฌ: Luis Serrano captivated our audience with an in-depth session on attention mechanisms and transformers. From self-attention to multi-headed attention and encoder/decoder architecture, participants unraveled the complexities of these vital components of LLMs.
๐. ๐๐ฏ๐จ๐ฅ๐ฎ๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐ฆ๐๐๐๐๐ข๐ง๐ ๐ฌ: Raja further provided a comprehensive review of classical text representation techniques and the power of semantic embeddings like Word2Vec. This session laid a solid theoretical foundation for understanding how embeddings have evolved in text analytics and NLP tasks.
๐. ๐๐๐ง๐๐ฌ-๐จ๐ง ๐๐ฑ๐๐ซ๐๐ข๐ฌ๐ ๐ฐ๐ข๐ญ๐ก ๐๐ -๐๐๐ & ๐๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ซ: The day concluded with a practical session where participants got their hands dirty with TF-IDF and Transformer exercises, including:
o Preparing datasets with vectors
o Writing data schemas for a vector database (using Redis)
o Storing the data and creating a vector search index
o Performing complex queries on a vector database including tag filters, numeric filters, text filters, geographic filters, combining filters, unions, range queries, and more.
By the end of the day, attendees walked away with a robust understanding of LLM architecture, embeddings, attention mechanisms, and practical skills in handling vector databases.
๐ ๐๐จ๐ข๐ง ๐ฎ๐ฌ ๐๐จ๐ซ ๐ญ๐ก๐ ๐ง๐๐ฑ๐ญ ๐๐จ๐ก๐จ๐ซ๐ญ ๐๐ง๐ ๐๐ ๐ฉ๐๐ซ๐ญ ๐จ๐ ๐ญ๐ก๐ข๐ฌ ๐ญ๐ซ๐๐ง๐ฌ๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐ฏ๐ ๐๐ ๐ฃ๐จ๐ฎ๐ซ๐ง๐๐ฒ: hubs.la/Q02DjWtK0