This article uses a visual presentation to help you deeply understand the structure of the LSTM model. Recently, I have been learning about the application of LSTM in time series prediction, but I encountered a significant problem: after adding time steps to the traditional BP network, its structure becomes difficult to understand.At the same time, … Read more
Source: Deep Learning Enthusiasts This article is about 3300 words, and it is recommended to read for more than 10 minutes. This article uses a visual presentation to help you deeply understand the structure of LSTM models. I have recently been learning about the application of LSTM in time series prediction, but I encountered a … Read more
Overview: In the latest study published on March 20 in the journal Nature, Grey Nearing and his colleagues from Google Research’s flood forecasting team demonstrated how to breakthrough by applying AI technology, significantly enhancing the timeliness and accuracy of global flood forecasts. Compared to the most advanced global flood forecasting system (GloFAS), this model uses … Read more
Traditional linear models struggle to solve multivariable or multi-input problems, while neural networks like LSTM excel at handling multiple variables. This characteristic makes them useful for addressing time series prediction issues. In the following article, you will learn how to build an LSTM model using the deep learning library Keras to tackle multivariable time series … Read more
WeChat ID: nsbdqk Long press to recognize the QR code to follow us Recommended Articles South-to-North Water Diversion and Water Conservancy Technology (Chinese and English) 2024 02 Prediction of Deep Foundation Pit Deformation Based on ESMD-FE-AJSO-LSTM Algorithm Editor’s Note The deformation of deep foundation pits excavated for sluices is influenced not only by objective factors … Read more
Article Title: BGP Anomaly Detection Using Graph Embedding and LSTM AutoencodersAuthors: Zhang Shuxiao, Tang Yong, Liu YujingAffiliation: College of Computer Science, National University of Defense TechnologyPublished: 2022, 31(2): 246–252Abstract In response to the problem of detecting BGP anomaly data, relying on publicly available real BGP update message data from the internet, we propose a new … Read more
Research on land subsidence intelligent prediction method based on LSTM and Transformer——A case study of Shanghai PENG Wenxiang1,2,3,4,5，ZHANG Deying1,2,3,4,5 1. Shanghai Institute of Geological Survey, Shanghai 200072; 2. Shanghai Institute of Geological Exploration Technology, Shanghai 200072; 3. Key Laboratory of Land Subsidence Monitoring and Prevention, Ministry of Natural Resources of China, Shanghai 200072; 4. Shanghai … Read more
Academic Dynamics Dynamic Learning Graph Convolution Mechanism for Short-Term Spatio-Temporal Traffic Flow Prediction Model Short-term traffic flow prediction is a core branch of intelligent transportation systems and plays an important role in traffic management. Graph Convolutional Networks (GCN) are widely used in traffic prediction models, effectively handling the graphical structure data of road networks. However, … Read more
This year, LSTM is trending! The original authors of LSTM have proposed xLSTM and Vision-LSTM, addressing previous limitations. At the same time, LSTM+Transformer has made it to Nature; various hybrid model architectures such as LSTM+CNN, LSTM+Attention are continuously refreshing SOTA. LSTM is definitely a great direction for generating ideas and publishing papers recently. I have … Read more
Author Michael NguyenTranslated by Wang Xiaoxin from Towards Data ScienceProduced by QbitAI | WeChat Official Account AI recognizes your voice, answers your questions, and helps you translate foreign languages, all relying on a special type of Recurrent Neural Network (RNN): Long Short-Term Memory Network (LSTM).Recently, there has been a very popular illustrated tutorial abroad about … Read more