Click on the above“Visual Learning for Beginners” to selectStar or “Top” Important information delivered promptly Source | Zhihu Author | Li Xinchun Source | https://zhuanlan.zhihu.com/p/106662375 For academic exchange only, please contact for removal if there is any infringement The Attention mechanism is a very important and effective technique in deep learning. This article will briefly … Read more
Follow the public account “ML_NLP” Set as “starred” to receive heavy content promptly! Author丨Electric Light Phantom Alchemy @ Zhihu Source丨https://zhuanlan.zhihu.com/p/362366192 Editor丨Machine Learning Algorithms and Natural Language Processing Attention has become a hot topic in the entire AI field, whether in machine vision or natural language processing, it is inseparable from Attention, transformer, or BERT. Below, … Read more
Follow the public account "ML_NLP" Set as “Starred“, heavy content will be delivered to you first! Author: Jiang Runyu, Harbin Institute of Technology SCIR Introduction In recent years, one of the most impressive achievements in the field of NLP is undoubtedly the pre-trained models represented by BERT proposed by Google. They continuously refresh records (both … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university professors, and corporate researchers. The community’s vision is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for beginners. Reprinted from | … Read more
Follow our public account to discover the beauty of CV technology 1 Introduction Due to its superior capability for modeling global dependencies, the Transformer and its variants have become the primary architecture for many visual and language tasks. However, tasks like Visual Question Answering (VQA) and Referencing Expression Comprehension (REC) often require multi-modal predictions that … Read more
Follow the public account “ML_NLP“ Set as “Starred“, delivering heavy content promptly! Original · Author | TheHonestBob School | Hebei University of Science and Technology Research Direction | Natural Language Processing 1. Introduction There are countless good articles online about this topic, all of which are very detailed. The reason I am writing this blog … Read more
Follow the WeChat public account “ML_NLP“ Set as “Starred“, delivering valuable content promptly! Source | Zhihu Address | https://zhuanlan.zhihu.com/p/47812375 Author | Jian Feng Editor | WeChat public account on Machine Learning Algorithms and Natural Language Processing This article is for academic sharing only. If there is any infringement, please contact us to delete the article. … Read more
Author: An Sheng & Yan Yongqiang, Datawhale Members This article has approximately10,000 words, divided into modules to interpret and practice the Transformer. It is recommended tosave and read. In 2017, Google proposed a model called Transformer in a paper titled “Attention Is All You Need,” which is based on the attention (self-attention mechanism) structure to … Read more
Source: AI Technology Online Today, I will share an article about the deep learning model Transformer. I would call it the best article explaining the Transformer model. The article mainly introduces the specific implementation of the Transformer model: Overall Architecture of Transformer Overview of Transformer Introduction to Tensors Self-Attention Mechanism Multi-Head Attention Mechanism Position-wise Feed-Forward … Read more
Author: Wang Chen, Who Asks Questions@Zhihu (Authorized) Source: https://www.zhihu.com/question/362131975/answer/3058958207 Editor: Jishi Platform Why Summarize Transformers Through Eighteen Questions? There are two reasons: First, the Transformer is the fourth major feature extractor after MLP, RNN, and CNN, also known as the fourth foundational model; the recently popular chatGPT is also built on the Transformer, highlighting its … Read more