【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
↑↑↑ Follow for “Star Mark“Kaggle Competition Guide Kaggle Competition Guide Author: Fareise, excerpted from Yuan Yuan’s Algorithm Notes Using RNN for Long-Term Time Series Forecasting, Is It Better Than Transformer SOTA? This article introduced today comes from South China University of Technology, proposing an RNN-based long-term time series forecasting model that outperforms the SOTA Transformer … Read more
Source | Zhihu Author | JerryFly Link | https://zhuanlan.zhihu.com/p/115823190 Editor | Deep Learning Matters WeChat Official Account This article is for academic exchange only. If there is any infringement, please contact us for deletion. RNN is commonly used to handle sequential problems. This article demonstrates the computation process of RNN using animated graphics. The three … Read more