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Attention in ML - Python Automation and Machine Learning for ICs - - An Online Book - |
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Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= In machine learning, attention is a mechanism that enables models to focus on specific parts of input data when making predictions or generating output. It is inspired by the way human attention works, where certain elements are prioritized while processing information. The attention mechanism is commonly used in sequence-to-sequence models, particularly in natural language processing tasks such as machine translation, text summarization, and image captioning. The idea is to allow the model to selectively attend to different parts of the input sequence when generating each element of the output sequence. The attention mechanism works by assigning weights to different elements of the input sequence based on their relevance to the current step of the output generation. These weights indicate the importance of each input element, and the model uses them to compute a weighted sum, giving more attention to the relevant parts of the input. There are different types of attention mechanisms, including:
Attention mechanisms have been successful in improving the performance of various machine learning models by enabling them to capture long-range dependencies and focus on relevant information. They have become a key component in state-of-the-art models, especially in tasks involving sequential data. ============================================
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