Transformers Are RNNs- Fast Autoregressive Transformers With Linear Attention
Offered By: Yannic Kilcher via YouTube
Course Description
Overview
Explore a comprehensive video explanation of the paper "Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention." Delve into the reformulation of the attention mechanism using kernel functions, resulting in a linear formulation that reduces computational and memory requirements. Discover the surprising connection between autoregressive transformers and RNNs. Learn about softmax attention, quadratic complexity, generalized attention mechanisms, kernels, linear attention, and experimental results. Gain insights into the intuition behind linear attention and understand the caveats of the RNN connection. This 48-minute video by Yannic Kilcher breaks down complex concepts, making them accessible to those interested in AI, attention mechanisms, transformers, and deep learning.
Syllabus
- Intro & Overview
- Softmax Attention & Transformers
- Quadratic Complexity of Softmax Attention
- Generalized Attention Mechanism
- Kernels
- Linear Attention
- Experiments
- Intuition on Linear Attention
- Connecting Autoregressive Transformers and RNNs
- Caveats with the RNN connection
- More Results & Conclusion
Taught by
Yannic Kilcher
Related Courses
Deep Learning for Natural Language ProcessingUniversity of Oxford via Independent Sequence Models
DeepLearning.AI via Coursera Deep Learning Part 1 (IITM)
Indian Institute of Technology Madras via Swayam Deep Learning - Part 1
Indian Institute of Technology, Ropar via Swayam Deep Learning - IIT Ropar
Indian Institute of Technology, Ropar via Swayam