In this video, we explore Transformers — the architecture behind modern AI and Large Language Models.
Understand attention, self-attention, and encoder-decoder models with clear intuition.
See how models process long sequences and generate text step-by-step.
A must-watch to strengthen your Deep Learning foundations.

🎯 Here’s What You’ll Learn in Deep Learning Part 4:

• Why RNNs and LSTMs struggle with long sequences
• The intuition behind the Attention mechanism
• Self-Attention explained step-by-step
• Query, Key, Value — what they actually mean
• How attention scores are calculated (with examples)
• Multi-Head Attention — why multiple heads exist
• Masked Attention and why models cannot see the future
• Encoder architecture — building contextual understanding
• Decoder architecture — generating sequences step by step
• Cross-Attention — how translation really works
• Feed Forward Networks inside Transformers
• Full Transformer architecture explained simply

📌 Timestamps –

00:00:00 – Introduction to Transformers & Deep Learning Concepts
00:02:11 – Quick Recap: CNN vs RNN vs ANN Explained
00:08:20 – What You Will Learn in This Complete Transformer Tutorial
00:10:20 – Encoder and Decoder Architecture Explained
00:59:41 – Attention Mechanism in Deep Learning
01:29:39 – Introduction to Transformers Architecture
01:43:11 – Multi-Head Attention Layer Explained
02:28:03 – Multi-Head Attention Summary & Intuition
02:34:20 – Before Learning Feed Forward Neural Networks (Important Concepts)
02:42:24 – Positional Encoding Explained with Intuition
02:51:05 – Feed Forward Neural Network inside Transformers
03:01:46 – Transformers Training & Testing Process
03:48:51 – Generative AI Announcement
03:49:20 – Outro & Final Thoughts


📘 Notes
https://drive.google.com/file/d/1j530XBAcZBMCCfHospM829jwtIES3Tkb/view?usp=sharing

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