Vector Embeddings, Positional Encoding & Self-Attention -From Scratch | скачать в хорошем качестве

Vector Embeddings, Positional Encoding & Self-Attention -From Scratch |

The Attention mechanism is the backbone of modern GenAI and Large Language Models (LLMs). In this video, we explain *Attention from scratch*, exactly as described in the paper *Attention Is All You Need*. You’ll understand how text flows through a Transformer — from embeddings to self-attention and softmax. 📌 Topics Covered (with timestamps): 00:00 Introduction 02:53 Vector Embeddings 09:33 Positional Encoding 12:11 Self Attention 15:25 Multi-Head Attention 17:13 Feed Forward Network 18:00 Linear Layer 19:13 Softmax Function By the end of this video, you’ll clearly understand: • Why embeddings are required • How positional encoding preserves word order • How self-attention works mathematically • Why multi-head attention is powerful • How softmax creates attention weights 📌 Reference Paper: 📄 Attention Is All You Need — Google Brain 📌 This video is part of the playlist: GenAI & LLM Mastery | Agentic AI, RAG, Prompt Engineering (Hands-On)    • GenAI & LLM Mastery | Hands-On with Agenti...   📂 Related Playlists: ▶️    • AWS Tutorial   ▶️    • Serverless   ▶️    • GenAI & LLM Mastery | Hands-On with Agenti...   🛠️ Who should watch? • Software Developers & Engineers • Backend / Cloud / Serverless Developers • GenAI & LLM beginners • Anyone preparing for AI / GenAI interviews 🔗 Follow & Connect with me: 👉 YouTube:    / @codingjist   👉 Instagram:   / codingjist   👉 LinkedIn:   / adityapatel143   👍 Like the video if it helped you 📩 Subscribe to *CodingJist* for more hands-on GenAI content #codingjist #ai #genai #systemdesign #aws #serverless #eventdriven #awslambda #cloudcomputing #microservices #devops #cloudarchitecture #techshorts #serverlessarchitecture #EventDrivenDesign #awsdeveloper #scalableapps #interview