Attention Mechanism, Positional Encoding & Transformer Architecture Explained

About this lesson

In this video, we will deeply understand the Transformer Architecture, including the core concepts of: ✅ Attention Mechanism (Self-Attention) ✅ Scaled Dot-Product Attention ✅ Multi-Head Attention ✅ Positional Encoding (Why it is needed?) ✅ Encoder-Decoder Transformer Architecture ✅ How Transformers work behind models like BERT, GPT, T5, and LLaMA This lecture is designed for students and researchers who want a clear and step-by-step explanation of transformers in Natural Language Processing (NLP). 📌 Topics Covered: Why RNN/LSTM fails in long sequences What is Attention and why it works Mathematical intuition of attention Positional encoding formula and intuition Full transformer pipeline (encoder + decoder) 🎯 Perfect for: AI / ML students, NLP learners, deep learning beginners, and university lectures. If you found this helpful, don’t forget to Like, Subscribe, and Share. #NLP #Transformers #AttentionMechanism #DeepLearning #MachineLearning #BERT #GPT

Original Description