Application of Attention Mechanism in “Attention is All You Need” 3.2.3 3.2.3 Application of Attention Mechanism in Our Model The Transformer uses three different ways of multi-head attention mechanism as follows: In the “encoder-decoder attention” layer, queries come from the previous layer of the decoder, while memory keys and values come from the output of … Read more
This article will cover three aspects of the essence of Transformer, the principles of Transformer, and the applications of Transformer, helping you understand Transformer (overall architecture & three types of attention layers) in one article. Transformer 1. Essence of Transformer The origin of Transformer:The Google Brain translation team proposed a novel simple network architecture called … Read more
Big Data Digest and Baidu NLP Jointly Produced Author: Damien Sileo Translators: Zhang Chi, Yi Hang, Long Xin Chen BERT is a natural language processing model recently proposed by Google, which performs exceptionally well in many tasks such as question answering, natural language inference, and paraphrasing, and it is open-source. Therefore, it is very popular … Read more
Click the card below to follow the “AI Frontier Express” public account Various important resources delivered promptly Reprinted from Zhihu: Yao Yuan Link: https://zhuanlan.zhihu.com/p/17311602488 1. Introduction The attention mechanism in Transformer [1] can effectively model the correlations between tokens, achieving significant performance improvements in many tasks. However, the attention mechanism itself does not have the … Read more
Machine Heart reports Editors: Danjiang, Xiaozhou SVM is all you need; Support Vector Machines are never out of date. The Transformer is a new theoretical model of Support Vector Machines (SVM) that has sparked discussion in academia. Last weekend, a paper from the University of Pennsylvania and the University of California, Riverside, sought to explore … Read more
This article aims to introduce the Transformer model. Originally developed for machine translation, this model has since been widely applied in various fields such as computer recognition and multimodal tasks. The Transformer model introduces self-attention mechanisms and positional encoding, and its architecture mainly consists of an input part, an output part, and encoders and decoders. … Read more
This is the fourth translation in the Illustrated Transformer series. The series is authored by Ketan Doshi and published on Medium. During the translation process, I modified some illustrations and optimized and supplemented some descriptions based on the code provided in Li Mu’s “Hands-On Deep Learning with Pytorch”. The original article link can be found … Read more
↑ ClickBlue Text Follow the Jishi Platform Author丨Genius Programmer Zhou Yifan Source丨Genius Programmer Zhou Yifan Editor丨Jishi Platform Jishi Guide In this article, the author presents a relatively complex self-attention replacement example project developed based on Diffusers, aimed at enhancing the consistency of SD video generation. Throughout this process, the author discusses the usage of AttentionProcessor-related … Read more
The hands-on deep learning live course has completed the first three parts! In the past 4 months, Dr. Mu Li, a senior chief scientist at Amazon has explained the basics of deep learning, convolutional neural networks, and computer vision. Since the course started, over 10,000 people have participated in the live learning, and the course … Read more
MLNLP ( Machine Learning Algorithms and Natural Language Processing ) community is a well-known natural language processing community both domestically and internationally, covering NLP graduate students, university teachers, and corporate researchers. The vision of the community is to promote communication between the academic and industrial circles of natural language processing and machine learning, as well … Read more