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Overview of Attention: Concept & Tool Deep Learning

Contributors
Total time needed: ~3 hours
Objectives
This Shortlist will cover what attention— a popular concept and a useful tool in deep learning—is. It will cover: Seq2Seq Models, Attention Mechanisms, Neural Turing Machines, and Transformers.
Potential Use Cases
Translation, Transformers, Generative Adversarial Network (GAN)
Who is This For ?
BEGINNERDeep learning developers interested in NLP
Click on each of the following annotated items to see details.
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ARTICLE 1. Attention? Attention!
  • What are attention mechanisms?
  • How was attention invented?
  • What are various attention mechanisms and models?
  • What’s wrong with Seq2Seq model?
  • What are neural turing machines?
  • What is a Pointer Network?
  • How can you build seq2seq models without recurrent network units?
  • What is a Self-Attention GAN?
40 minutes
VIDEO 2. DeepMind x UCL | Deep Learning Lectures | 8/12 | Attention and Memory in Deep Learning
  • What are some contemporary attention mechanisms?
  • What is the implicit attention present in any deep network?
  • What are discrete and differentiable variants of explicit attention?
  • How do networks with external memory work and how can attention provide them with selective recall?
90 minutes
ARTICLE 3. Attention Mechanism in Neural Networks
  • What's the difference between global and local attention?
  • Why is local attention also called window-based attention?
10 minutes

Concepts Covered

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