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Attention mechanism is key in Transformer models. It's a big idea in recent years but not easy to understand. In this video, I explain the attention mechanism from the start. We'll look at Scaled Dot Product Attention, and how Key, Query, and Value matrices are trained. Plus, we'll see how these parts make up an Attention Head or Attention Layer. If you're interested in AI and Transformers, this video makes it easy to understand this important part. 📹 Video about the Transformer Architecture: • Transformers Explained by Example 🙏 Support me: / kiecodes 🛰 Join our Discord, to interact with other Coders and me: / discord 🧠 Pick my brain: https://calendly.com/kiecodes/ai-cons... Check out my newest video: • How to use the OpenAI API in Python Timestamps: 00:00 Intro 00:22 What is Attention 00:55 What are Attention Layers or Attention Heads 02:55 What is Multi-Head Attention Layer 04:23 What's in an Attention Layer 05:00 The Attention Function 11:36 Normalisation 14:19 Putting it all together 16:07 Masked Attention 17:48 Cross Attention --- This video contains advertising content. --- Attribution: ■ Video von Tima Miroshnichenko: https://www.pexels.com/de-de/video/pe... ■ Video von Tima Miroshnichenko: https://www.pexels.com/de-de/video/ma... ■ Brain icons created by Freepik - Flaticon: https://www.flaticon.com/free-icons/b...