Transformer Deep Dive 🤖⚙️ (Based on “Attention Is All You Need”) скачать в хорошем качестве

Transformer Deep Dive 🤖⚙️ (Based on “Attention Is All You Need”)

🤖✨ Transformer finally makes sense — in this video I explain the Transformer architecture step by step, based on the original paper “Attention Is All You Need.” No fluff, just a clear big picture and the core mechanics. 📌 What you’ll learn in this video • Where Transformer sits in AI and why it became a foundation architecture 🌍 • The motivation: why RNNs & CNNs struggled (sequential ops + long-range dependencies) 🚧 • The high-level encoder–decoder structure 🏗️ • Scaled dot-product attention (Q, K, V) explained clearly 🔎 • Multi-head attention: why multiple heads matter 👀👀 • The 3 ways attention is used: encoder self-attention, decoder masked self-attention, cross-attention 🔁 • Position-wise feed-forward networks (FFN) ⚙️ • Positional encoding: why we need it, and why sine/cosine helps 📍🌊 • Embeddings + weight sharing for efficiency 🧩 • Final recap: why Transformer won 🏁