Author: Chen Zhi Yan This article is approximately 3500 words long and is recommended for a 7-minute read. The Transformer is the first model that completely relies on the self-attention mechanism to compute its input and output representations. The mainstream sequence-to-sequence models are based on encoder-decoder recurrent or convolutional neural networks. The introduction of the … Read more
Click on the above “Beginner Learning Visuals” to select “Star” or “Pin” Important content delivered promptly Author: Wang Bo Kings, Sophia Overview of the Content of This Article: Wang Bo Kings’ Recent Learning Notes on Transformer Recommended AI Doctor Notes Series Weekly Zhi Hua’s “Machine Learning” Handwritten Notes Officially Open Source! Printable version with PDF … Read more
Click on the above “Beginner’s Visual Learning” to select “Add to Favorites” or “Pin” Essential content delivered immediately Source: Python Data Science This article is about 7200 words long and is recommended to read in 14 minutes. In this article, we will explore the Transformer model and understand how it works. 1. Introduction Google’s BERT … Read more
Understanding Transformers Through Llama Model Architecture Llama Nuts and Bolts is an open-source project on GitHub that rewrites the inference process of the Llama 3.1 8B-Instruct model (80 billion parameters) from scratch using the Go language. The author is Adil Alper DALKIRAN from Turkey. If you are interested in how LLMs (Large Language Models) and … Read more
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
Machine Heart Reports Editor: Zhao Yang Recently, a paper was published on arXiv, providing a new interpretation of the mathematical principles behind Transformers. The content is extensive and rich in knowledge, and I highly recommend reading the original. In 2017, Vaswani et al. published “Attention Is All You Need,” marking a significant milestone in the … Read more
Introduction In recent years, deep learning has made tremendous progress in the field of Natural Language Processing (NLP), and the Transformer model is undoubtedly one of the best. Since the Google research team proposed the Transformer model in their paper “Attention is All You Need” in 2017, it has become the cornerstone for many NLP … 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 teachers, and corporate researchers. The community’s vision is to promote communication and progress among the academic and industrial circles of natural language processing and machine learning, especially for beginners. Reprinted from | … Read more
Source | Zhihu Author | JayLou Link | https://zhuanlan.zhihu.com/p/53682800 Editor | Deep Learning Matters WeChat Public Account This article is for academic sharing only. If there is any infringement, please contact us to delete. This article summarizes the attention mechanism (Attention) in natural language processing in a Q&A format and provides an in-depth analysis of … Read more
Question: I have searched various materials and read the original papers, which detail how Q, K, and V are obtained through certain operations to derive output results. However, I have not found any explanation of where Q, K, and V come from. Isn’t the input to a layer just a tensor? Why do we have … Read more