Last week, we launched "Attention in Transformers: Concepts and Code in PyTorch" instructed by @JoshuaStarmer! In this course, you'll: ✅ Learn how the attention mechanism in LLMs helps convert base token embeddings into rich context-aware embeddings. ✅ Understand the Query, Key, and Value matrices, what they are for, how to produce them, and how to use them in attention. ✅ Learn the difference between self-attention, masked self-attention, and cross-attention, and how multi-head attention scales the algorithm. 🔗 Enroll for free: hubs.la/Q037ytWB0

@DeepLearningAI @joshuastarmer The launch of "Attention in Transformers" is a pivotal moment for anyone diving into deep learning. Understanding attention mechanisms is essential; it's where AI begins to truly understand context.

@DeepLearningAI @joshuastarmer You mean THE Josh Starmer from StatQuest ?! StaaatQuest 🎵

@DeepLearningAI @joshuastarmer attentions key for AI success. many struggle with clear strategies and legacy tech. we help bridge that gap.

@DeepLearningAI @joshuastarmer Attention mechanisms in LLMs are a game changer, been experimenting with them in my own projects. The way they help convert base token embeddings into rich context-aware embeddings is pure magic. Definitely going to check out this course, thanks for sharing @DeepLearningAI