Post One of the key moments when I truly understood how transformers work: 🧠✨

One of the key moments when I truly understood how transformers work: 🧠✨ "Stop thinking of a transformer as a conveyor belt, where each layer transforms the output of the previous one." "Start thinking of it as a residual flow." 🌊 Each block in a transformer has a residual connection that adds the input of the block to its output: x' = f(x) + x Because of this addition, the attention mechanism or MLP within the layer – the function f in the formula above – actually does NOT transform the input. Instead, it calculates the information that needs to be ADDED to the input before passing it to the next block! ➕ Imagine the main part of the transformer as a shared whiteboard. 📝 Each block reads what it needs from it and adds its own notes. All changes are additions. Furthermore, layers can exchange information over distances. A block in the first layer can write information, and a block in the fifth layer can read it, even though there is no direct connection between them. 🔗 I owe these ideas to an older article by Anthropic about the architecture of transformers: transformer-circuits.pub/2021/framework #Transformers #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A