Self Attention | Generative Ai | Basic to Advance

Sign up with Euron today : https://euron.one/sign-up?ref=430A5AD3 Welcome to Learning Logic with Prince Katiyar! Are you ready to dive deep into the transformative world of self-attention and unlock your potential in the tech world? In this engaging video, we unravel the complexities of self-attention, a game-changing concept crucial for understanding cutting-edge AI technology. Discover the truth behind why this mechanism is essential for handling contextual embeddings and parallel data processing, transforming the way AI models like transformers operate. Explore now and learn how these insights can revolutionize your coding projects and tech career. Don't miss out on this opportunity to gain actionable insights and advance your skills. Watch now, and if you believe in these changes, share this video to reach as many people as possible. Subscribe to Learning Logic, hit the bell icon for the latest updates, and join the conversation in the comments below! #AI #ArtificialIntelligence #coding #techupdates #Programing #SkillDevelopment #CareerJourneyTips #nlp #datascience #naturallanguageprocessing #contextualembeddings #layernormalization Material Link : https://euron.one/course/generative-ai-basic-to-advance CHAPTERS: 00:00 - Introduction 00:29 - What is Self Attention 01:59 - What is Word Embedding 02:35 - Understanding Attention Mechanism 03:30 - Did You Understand? 05:38 - Exploring Self Attention 07:56 - Static Embedding Explained 08:58 - Average Embedding Overview 11:28 - Dynamic Embedding Insights 13:19 - Contextual Embedding Explained 14:28 - Relation Extraction Techniques 16:39 - Word Relations Explained 18:19 - Role of Query in Attention 20:15 - Mathematical Aspects of Query 21:50 - Achieving Mathematical Understanding 23:50 - Contextual Embedding Revisited 26:20 - Embeddings in Ajio 27:28 - Step 2 - Value Calculation Process 30:26 - Step 3 - Backpropagation Explained 31:19 - Contextual Embedding Insights 32:03 - Understanding Parallelism 33:40 - Last Minute Problem Discussion 36:11 - Multiple Meanings of Words 36:35 - Problem Number 2 Overview 37:55 - Tension Mechanism Explained 38:22 - Understanding Attention 39:54 - Problem 1 Discussion 41:40 - Contextual Meaning Insights 42:40 - Model Learning Issues 44:40 - Constant Vector Explained 46:15 - Linear Transformation of Embeddings 47:30 - Linear Transformation of Embeddings - Part 2 48:55 - Linear Transformation of Embeddings - Part 3 50:20 - Amazon Query - Part 2 51:25 - Amazon Query - Part 3 56:15 - Problem 2 - Multiplication Range 57:13 - Problem 3 - Exploding Gradients 59:14 - Scaled Dot Product Explained 1:00:48 - Final Thoughts 1:01:00 - Recap of Key Points 1:01:30 - Problem-Solving Summary 1:02:50 - Final Point Discussion 1:03:40 - Closing Remarks Instagram: https://www.instagram.com/euron_official/?igsh=Z3A3cWgzdjEzaGl4&utm_source=qr YouTube: https://www.youtube.com/channel/UCZBfu59WmdZ5P7__xAEN4Xw WhatsApp :https://whatsapp.com/channel/0029VaeeJwq9RZAfPW9P2l07 LinkedIn: https://www.linkedin.com/company/euronone/?viewAsMember=true Facebook: https://www.facebook.com/people/EURON/61566117690191/ Twitter :https://x.com/i/flow/login?redirect_after_login=%2Feuron712