12. Positional Encoding in Transformers Explained | Transformer Architecture In Hindi

AI SayI · Beginner ·🧠 Large Language Models ·6mo ago

About this lesson

Why do Transformers process data in parallel but still understand the order of words? In this video, we break down the critical role of Positional Encoding. We explore how this technique allows models like GPT and BERT to capture sequence structure and relative positions of elements without using recurrence. What you’ll learn in this video: Why Transformers don’t inherently understand token order. How adding a unique vector to each token embedding preserves position data. The difference between Sinusoidal encoding and Learnable embeddings. Why sequence structure is vital for distinguishing phrases like "cat sat on the mat" vs. "mat on the sat cat." If you’re studying Deep Learning or Natural Language Processing (NLP), this breakdown will help you understand one of the most important components of the Transformer architecture.

Original Description

Why do Transformers process data in parallel but still understand the order of words? In this video, we break down the critical role of Positional Encoding. We explore how this technique allows models like GPT and BERT to capture sequence structure and relative positions of elements without using recurrence. What you’ll learn in this video: Why Transformers don’t inherently understand token order. How adding a unique vector to each token embedding preserves position data. The difference between Sinusoidal encoding and Learnable embeddings. Why sequence structure is vital for distinguishing phrases like "cat sat on the mat" vs. "mat on the sat cat." If you’re studying Deep Learning or Natural Language Processing (NLP), this breakdown will help you understand one of the most important components of the Transformer architecture.
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