Transformers
Demystifying Transformers in Data Science
Understanding Transformers
Transformers are a type of deep learning model that is designed to handle sequential data more effectively than previous architectures. They were introduced in a groundbreaking paper titled "Attention is All You Need" by Vaswani et al. in 2017. The architecture's key innovation is its attention mechanism, which allows it to weigh the significance of different parts of the input data when making predictions.
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Applications of Transformers
Transformers have been widely used in various domains, such as natural language processing (NLP), where they have demonstrated state-of-the-art performance in tasks like language translation, sentiment analysis, and text generation. They are also utilized in speech recognition, time series analysis, and image recognition tasks, showcasing their versatility in handling different types of sequential data.
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References
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Vaswani, A., et al. "Attention is All You Need." Advances in Neural Information Processing Systems, 2017.
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Devlin, J., et al. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." arXiv preprint arXiv:1810.04805, 2018.
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Brown, T. B., et al. "Language Models are Unsupervised Multitask Learners." OpenAI Blog, 2019.
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Raffel, C., et al. "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer." Journal of Machine Learning Research, 2020.
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Dosovitskiy, A., et al. "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale." arXiv preprint arXiv:2010.11929, 2020.
