\mathcal{L}_{\text{MLM}}(x) = - \frac{1}{|M_x|} \sum_{i \in M_x} \log P(x_i | x \setminus M_x) \quad \tag{4.1} 图4.1 展示了 MLM 任务,其中 x 和 y 分别表示不同语言的单语输入句子。尽管该任务没有考虑汇总的掩码输入中多语言的信息,但它在生成跨语言对齐的表示方面表现出...
Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers. Key Features: A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book ...
https://www.deeplearning.ai/short-courses/how-transformer-llms-work/ 介绍《Transformer大语言模型工作原理》课程,该课程由《动手实践大语言模型》一书作者Jay Alammar和Maarten Grootendorst联合打造。本课程深入探讨了支撑大语言模型(LLMs)的transformer架构的主要组件。 transformer架构彻底改变了生成式AI。事实上,...
《How Transformers work in deep learning and NLP: an intuitive introduction》 O网页链接 《Transformers From Scratch》O网页链接《A Deep Dive Into the Transformer Architecture – The Development of Transformer Models》 O网页链接《A Survey on Transformer Models in Machine Learning》O网页链接...
3. 《A Deep Dive Into the Transformer Architecture – The Development of Transformer Models》[link] 4. 《A Survey on Transformer Models in Machine Learning》[link] 5. 《Deep Learning for Natural Language Processing - YouTube》[link]
AI Dataset Services for Machine Learning Understanding the Transformer Model The Transformer model is a deep learning model introduced in 2017 by Vaswani et al. in a seminal paper titled “Attention is All You Need”. The model revolutionized the field of Natural Language Processing (NLP) and has...
Residual Connection is a critical component in deep neural networks that mitigates the challenges of training very deep architectures. As we increase the depth of a neural network by stacking more layers we bump into the problem of vanishing/exploding gradients, where in case of vanishing gradien...
In this guide, we explore what Transformers are, why Transformers are so important in computer vision, and how they work.
in machine learning and artificial intelligence. There’s no better time than now to gain a deep understanding of the inner workings of transformer architectures, especially with transformer models making big inroads into diverse new applications likepredicting chemical reactionsandreinforcement learning. ...
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