Applied Deep Learning – Class 34 | Encode_Decoder_Theory

Описание: In this session of Applied Deep Learning, we dive deep into the Encoder–Decoder Architecture — covering the full theory and intuition behind this fundamental deep learning model.

This lecture focuses solely on conceptual understanding (no coding), so you can build a strong foundation before moving to implementations.

📚 In this lecture, we cover:

🔹 What is an Encoder–Decoder architecture
🔹 How the Encoder processes input sequences
🔹 How the Decoder generates output sequences
🔹 Sequence-to-sequence learning explained
🔹 Information flow and representation transformation
🔹 Challenges in vanilla Encoder–Decoder models
🔹 Why this architecture is powerful for real-world tasks

📌 Applications where Encoder–Decoder is widely used:

✔ Machine translation
✔ Text summarization
✔ Chatbots and conversational AI
✔ Speech recognition
✔ Sequence generation tasks

Understanding the Encoder–Decoder framework is essential before advancing to attention mechanisms, transformers, and modern sequence modeling approaches.

If you're learning NLP, deep learning, or preparing for interviews, this session gives you the theoretical clarity you need.

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

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