This article is a translated version of the notes from Stanford University’s CS224d course, authorized by Professor Richard Socher of Stanford University. Unauthorized reproduction is prohibited; for specific reproduction requirements, please see the end of the article. Translation: Hu Yang & Xu Ke Proofreading: Han Xiaoyang & Long Xincheng Editor’s Note: This article is the … Read more
Source: Panchuang AI, Author: VK Panchuang AI Share Author | Renu Khandelwal Compiler | VK Source | Medium We start with the following questions: Recurrent Neural Networks can solve the problems present in Artificial Neural Networks and Convolutional Neural Networks. Where can RNNs be used? What is RNN and how does it work? Challenges of … Read more
【Introduction】This article provides the background and overview of Dropout, as well as a parameter analysis of its application in language modeling using LSTM / GRU recurrent neural networks. Author｜Adrian G Compiler｜Zhuanzhi (No secondary reproduction), Xiaoshi Organizer｜Yingying Dropout Inspired by the role of gender in evolution, Hinton et al. first proposed Dropout, which temporarily removes units … Read more
Introduction I rarely write summary articles, but I feel it’s necessary to periodically summarize some interconnected knowledge points, so I’ve written this one. Since my content mainly focuses on time series and spatio-temporal prediction, I will primarily discuss RNN, LSTM, GRU, ConvLSTM, ConvGRU, and ST-LSTM. 1. RNN The most primitive recurrent neural network, essentially a … Read more
Neural networks are the backbone of deep learning, and among the various neural network models, RNNs are the most classic. Despite their imperfections, they possess the ability to learn from historical information. Subsequent frameworks, whether the encode-decode framework, attention models, self-attention models, or the more powerful Bert model family, have evolved and strengthened by standing … Read more
Machine Heart Column Author:Zhang Hao RNNs are very successful in handling sequential data. However, understanding RNNs and their variants, LSTM and GRU, remains a challenging task. This article introduces a simple and universal method for understanding LSTM and GRU. By simplifying the mathematical formalization of LSTM and GRU three times, we can visualize the data … Read more
Selected from arXiv Authors:Jos van der Westhuizen, Joan Lasenby Compiled by Machine Heart Contributors: Pedro, Lu This paper studies what happens when LSTM only has a forget gate and proposes JANET, with experiments showing that this model outperforms standard LSTM. 1. Introduction Excellent engineers ensure their designs are practical. We now know that the best … Read more
Follow the public account “ML_NLP“ Set as “Starred“, heavy content delivered first-hand! From | Zhihu Author | Tian Yu Su https://www.zhihu.com/question/278825804/answer/402634502 Editor | Deep Learning This Small Matter Public Account This article is for academic exchange only. If there is any infringement, please contact the background for deletion. I have done some similar work, let … Read more
Source: Machine Heart This article contains 3744 words, and is recommended for a reading time of 8 minutes. Through this article, we will explain how to build models for natural language, audio, and other sequential data. Since Andrew Ng released the deeplearning.ai courses, many learners have completed all the specialized courses and meticulously created course … Read more
Reading time required 6 minutes Speed reading only takes 2 minutes Please respect the original labor resultsReprint must indicate the link to this articleand the author: Machine Learning Heart Click to read the original text or copy the following link to the browser to obtain the complete source code and data of the article: https://mbd.pub/o/bread/mbd-Z56Ul5hy … Read more