Applied Deep Learning – Class 39 | Need for self Attention скачать в хорошем качестве

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... 👍 Like, Share & Subscribe for more AI & Deep Learning content #DeepLearning #SelfAttention #WordEmbeddings #BOW #OneHotEncoding #NLP #MachineLearning #AI #AppliedDeepLearning