📐 Encoding Word Positions – Live Coding with Sebastian Raschka (Chapter 2.8) скачать в хорошем качестве

📐 Encoding Word Positions – Live Coding with Sebastian Raschka (Chapter 2.8)

Check out Sebastian Raschka's book 📖 Build a Large Language Model (From Scratch) | https://hubs.la/Q03l0mSf0 📖 How do transformer models understand the order of words in a sentence? In this live-coding session, AI & LLM engineer ‪@SebastianRaschka‬ unpacks Chapter 2.8: Encoding Word Positions from his Manning book, Build a Large Language Model (From Scratch). Learn how to implement positional encodings, the mechanism that gives transformers a sense of sequence without recurrence. 0:00 - Introduction to Positional Embeddings 2:15 - Transition from Token IDs to Embeddings 4:01 - Implementing Positional Embeddings 6:04 - Combining Token and Positional Embeddings 11:09 - Conclusion and Forward Look 📘 About the Book Build a Large Language Model (From Scratch) is a practical and eminently satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you’ll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you’ll really understand it because you built it yourself! 💬 Perfect for ML engineers, researchers, and curious developers who want to demystify how language models actually work. 🔗 Get the Book: https://hubs.la/Q03l0mSf0 📺 Subscribe for more hands-on coding sessions and chapter walkthroughs from top Manning authors. #SebastianRaschka #LLM #PositionalEncoding #Transformers #DeepLearning #PyTorch #MachineLearning #NLP #ManningPublications #LiveCoding