Attention in machine learning is a technique that mimics cognitive attention in artificial neural networks. It enhances some parts of the input data while diminishing others, so that the network can focus more on the important parts of the data, even if they are a small portion of an image or sentence. The context determines which part of the data is more important than another, and this is trained by gradient descent.
There are different applications of attention in AI, including:
- Sales: Attention is an AI platform that fills your CRM and coaches your sales reps to turn them into top performers
- Pre-launch Analytics: Attention Insight’s AI predicts changes in visual attention based on 70,000 images from eye-tracking studies
- Sequence Models: Attention can be used to improve the performance of the encoder-decoder model for machine translations. It allows the decoder to utilize the most relevant parts of the input sequence in a flexible manner, by a weighted combination of all the encoded input vectors, with the most relevant vectors being attributed the highest weights.
Some resources for learning more about attention in machine learning include:
- The TensorFlow tutorial on attention
- Alex Graves’ video from DeepMind
- The Illustrated Transformer by Jay Alammar
- The Attention Mechanism from Scratch by Jason Brownlee.
