Transformers have undoubtedly transformed the field of Natural Language Processing. Their attention mechanisms and multi-head attention architecture have propelled them into the limelight, enabling faster training, improved performance, and broader applicability. Whether you’re a novice or an expert, grasping the core concepts of transformers is essential for staying at the forefront of the machine learning landscape. As you embark on your journey into the world of transformers, remember that understanding the foundational principles is the first step towards harnessing their immense potential in shaping the future of NLP and beyond.
And our guide, Transformers in Action, is here to take you along that path.
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From Automated Machine Learning in Action by Qingquan Song, Haifeng Jin, and Xia Hu
This article covers
• Defining and introducing the fundamental concepts of machine learning
• Describing the motivation for and high-level concepts of automated machine learning