Linear Attention Explained from First Principles (Transformers → RNNs) скачать в хорошем качестве

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 #machinelearning #deeplearning #attentionmechanism #transformers #linearattention #artificialintelligence #aiexplained #neuralnetworks #mlresearch #efficientai #aiarchitecture #learnai #computerscience #DataScience #AIEngineering #TransformerModels #generativeai #AITutorial #techeducation #futureofai