Скачать или смотреть Linear Attention Explained from First Principles (Transformers → RNNs)

🚀 Attention changed machine learning forever.

From GPT models to image generation and video AI, attention powers almost every modern deep learning system. But there’s a problem…

⚠️ Standard attention scales quadratically, making long sequences expensive and memory-intensive.

In this video, we build intuition step by step and understand:

✅ What attention really is (beyond equations)
✅ Query, Key, and Value intuition
✅ How tokens communicate through similarity
✅ Why softmax attention becomes computationally expensive
✅ The core idea behind linear attention
✅ Kernel trick intuition in transformers
✅ How linear attention reduces complexity from O(N²) → O(N)
✅ Causal attention explained clearly
✅ Why linear transformers behave like Recurrent Neural Networks

Instead of memorizing formulas, we focus on first-principles understanding so you can deeply grasp how modern sequence models work.

💡 Key Topics Covered

Transformer Attention Mechanism
Scaled Dot Product Attention
Linear Attention
Efficient Transformers
Kernel Methods in Deep Learning
Causal Attention
Transformers vs RNNs
Sequence Modeling

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