Скачать или смотреть Applied Deep Learning – Class 39 | Need for self Attention

In this session of Applied Deep Learning, we introduce the concept of Self-Attention by first understanding how text is represented as vectors — from basic mappings like one-hot encoding to powerful representations like word embeddings.

This lecture is theory-first, designed to build your intuition about text representation and the need for self-attention before implementation.

📚 In this lecture, we cover:
🔹 1. One-Hot Encoding

How it represents words as vectors

Limitations:
➤ No semantic meaning
➤ High dimensionality

🔹 2. Bag-of-Words (BoW)

Simple vector representation

Still lacks context and semantics

🔹 3. Need for Better Representations

These basic vector techniques treat words independently — they do not capture similarity or meaning.

🔹 4. Introduction to Word Embeddings

What embeddings are

How they encode semantics

Why they are better than one-hot / BoW

🔹 5. Why Self-Attention?

Word embeddings bring semantics, but they still lack contextual understanding — the meaning of a word depending on its surrounding words. That’s where Self-Attention comes in.

In the next class, we will study how Self-Attention generates contextual embeddings — taking word meaning in context rather than in isolation.

📂 Notebook Link:
https://github.com/GenEd-Tech/Applied...

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