Transformer Architecture Explained | The Foundation of Large Language Models (LLMs) | Hindi скачать в хорошем качестве

Transformer Architecture Explained | The Foundation of Large Language Models (LLMs) | Hindi

🚀 In this video, we dive deep into the Transformer architecture, the foundation of modern AI and Large Language Models (LLMs) like GPT, BERT, and T5. If you’re curious about how AI understands and generates human-like text, this lecture will give you a detailed breakdown of the Transformer model, including: ✅ Introduction to Large Language Models (LLMs) ✅ The Transformer Architecture – Encoder & Decoder ✅ Self-Attention & Multi-Head Attention Mechanism ✅ Positional Encoding & Feed Forward Networks ✅ Masked Attention ✅ Linear & Softmax Layers for Token Prediction 🧠 By the end of this video, you'll understand how Transformers power AI models like ChatGPT, Gemini. 🎯 Timestamps (For Better Navigation) 00:00 – Introduction to Large Language Models (LLMs) 05:21— What is the Transformer Model? 08:29— Input Embedding 13:57 – Positional Encoding 22:22— Multi-Head Attention 26:29 – Add and Norm 28:50 – Feed Forward Network 31:01 – Right Shift 33:45 – Masked Multi-Head ATtention 35:22 – Linear & Softmax Layers (Token Prediction) 🔍 Want to Learn More? 📖 Recommended Reading: Attention Is All You Need (Original Paper) 💡 Join the Community 🌍 Follow for More AI & NLP Content 📌 Website: https://easyexamnotes.com 📌 FaceBook: @easyexamnotes 📌 LinkedIn: EasyExamNotes 🙌 Like, Subscribe & Hit the Bell Icon 🔔 If you found this lecture helpful, don’t forget to like the video, subscribe to the channel, and comment below with your thoughts or questions!