Transformers and Attention in Details | شرح بالتفصيل

هل تريد معرفة تفاصيل الـ Transformers خطوة بخطوة و كيف ساهمت في حل الكثير من مشاكل الذكاء الإصطناعي مع البيانات؟ انضم الينا في هذا الفيديو لتحميل العرض التوضيحي | Slides https://drive.google.com/file/d/1uSHTU5vfwesPX0QrJCh-IZ7qyoWc7qUj/view?usp=sharing Explore the fascinating world of Transformers and Attention mechanisms in this comprehensive video! Whether you're a tech enthusiast or just curious about artificial intelligence, we delve into the intricacies of how Transformers revolutionized the field. From their inception to their impact on natural language processing and beyond, join us on a journey through the layers of attention in neural networks. 00:00 introduction 02:21 How Does RNN Work 04:51 RNN Limitations 07:40 Vectors 12:18 Tokenization 14:18 Embeddings 15:43 Positional Embeddings 18:55 Self Attention 25:05 Multi-Head Attention 32:01 What Key, Query, and Value? 36:48 Masked Multi-Head Attention 42:04 How to Train a Transformer 48:54 Transformer Inference 53:22 Why Encoder + Decoder 54:18 Transformers as Embedding Models 55:50 Greedy Search 56:50 Conclusion