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Transformer 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 | ||||||||
================================================================================= Transformer in machine learning refers to a specific type of neural network architecture introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017 [1]. Transformers have become a popular and powerful architecture for various natural language processing (NLP) tasks, such as language translation, text summarization, and sentiment analysis. Transformers utilize self-attention mechanisms to process input sequences in parallel, allowing them to capture long-range dependencies and relationships in the data.
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[1] Vaswani et al., 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.
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