Efficient Self-Attention for Transformers

Machine Learning Studio · Intermediate ·🧬 Deep Learning ·2y ago

About this lesson

The memory and computational demands of the original attention mechanism increase quadratically as sequence length grows, rendering it impractical for longer sequences. However, various methods have been developed to streamline the attention mechanism's complexity. In this video, we'll explore some of the most prominent models that address this challenge. #transformers Link to the activation function video: A Review of 10 Most Popular Activation Functions in Neural Networks https://youtu.be/56ZxEmGRt2k

Original Description

The memory and computational demands of the original attention mechanism increase quadratically as sequence length grows, rendering it impractical for longer sequences. However, various methods have been developed to streamline the attention mechanism's complexity. In this video, we'll explore some of the most prominent models that address this challenge. #transformers Link to the activation function video: A Review of 10 Most Popular Activation Functions in Neural Networks https://youtu.be/56ZxEmGRt2k
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