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LSTM

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Test Development Learning Exchange
Test Development Learning Exchange
Nov 29, 2024 · Artificial Intelligence

Using LSTM Networks for Stock Price Time Series Prediction with Keras

This tutorial demonstrates how to apply an LSTM deep‑learning model in Python to forecast stock closing prices, covering data acquisition, preprocessing, model construction, training, evaluation, and visualization of results for time‑series prediction.

AIKerasLSTM
0 likes · 8 min read
Using LSTM Networks for Stock Price Time Series Prediction with Keras
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Nov 12, 2023 · Artificial Intelligence

A Comprehensive Introduction to RNN, LSTM, Attention Mechanisms, and Transformers for Large Language Models

This article provides a thorough overview of large language models, explaining the relationship between NLP and LLMs, the evolution from RNN to LSTM, the fundamentals of attention mechanisms, and the architecture and operation of Transformer models, all illustrated with clear examples and diagrams.

Artificial IntelligenceLSTMNLP
0 likes · 25 min read
A Comprehensive Introduction to RNN, LSTM, Attention Mechanisms, and Transformers for Large Language Models
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Oct 21, 2023 · Artificial Intelligence

Understanding LSTM, ELMO, and Transformer Models for Natural Language Processing

This article explains the principles and structures of LSTM networks, introduces the ELMO contextual embedding model with its two‑stage pre‑training and downstream usage, and provides an overview of the Transformer architecture, highlighting their roles in modern NLP tasks.

ELMoLSTMNLP
0 likes · 12 min read
Understanding LSTM, ELMO, and Transformer Models for Natural Language Processing
DataFunTalk
DataFunTalk
Oct 16, 2023 · Artificial Intelligence

Personalized Title Generation and Automatic Cover Image Synthesis for Information‑Flow Scenarios

This article presents technical approaches for generating personalized article titles and automatically synthesizing cover images, covering keyword‑based, click‑sequence‑based, and author‑style‑based title models, as well as image restoration, key‑information extraction, object detection, and layout generation techniques to improve user engagement in recommendation and search systems.

AI recommendationLSTMTransformer
0 likes · 11 min read
Personalized Title Generation and Automatic Cover Image Synthesis for Information‑Flow Scenarios
Ctrip Technology
Ctrip Technology
Aug 3, 2023 · Operations

Intelligent Anomaly Detection for Ctrip Operations: LSTM Forecasting, Trend Analysis, Adaptive Thresholds, and Periodic Anomaly Filtering

The article describes Ctrip's AIOps approach to improving alert quality by combining statistical methods and machine‑learning models such as LSTM, trend analysis, adaptive threshold calculation, and dynamic‑time‑warping based periodic anomaly detection, achieving significant gains in precision and fault‑recall rates.

AIOpsAnomaly DetectionLSTM
0 likes · 12 min read
Intelligent Anomaly Detection for Ctrip Operations: LSTM Forecasting, Trend Analysis, Adaptive Thresholds, and Periodic Anomaly Filtering
Model Perspective
Model Perspective
Aug 1, 2023 · Artificial Intelligence

Mastering LSTM: How Long Short-Term Memory Networks Capture Long-Term Dependencies

This article explains the challenges of processing sequential data, introduces LSTM as a solution to long‑term dependency problems in RNNs, details its cell state and gate mechanisms, showcases its architecture, and provides Python code examples for time‑series forecasting using Keras.

KerasLSTMPython
0 likes · 9 min read
Mastering LSTM: How Long Short-Term Memory Networks Capture Long-Term Dependencies
Ctrip Technology
Ctrip Technology
May 18, 2023 · Artificial Intelligence

LSTM‑Based Advertising Inventory Forecasting with Embedding and Incremental Training at Ctrip

This article presents Ctrip's end‑to‑end solution for precise ad‑inventory forecasting using an LSTM model combined with entity embedding, covering data preprocessing, K‑means clustering, model architecture, offline‑online incremental training, early‑stop mechanisms, evaluation metrics, and Python service deployment.

Big DataLSTMPyTorch
0 likes · 19 min read
LSTM‑Based Advertising Inventory Forecasting with Embedding and Incremental Training at Ctrip
DataFunTalk
DataFunTalk
Apr 3, 2023 · Artificial Intelligence

Implementing RNN, LSTM, and GRU with PyTorch

This article introduces the basic architectures of recurrent neural networks (RNN), LSTM, and GRU, explains PyTorch APIs such as nn.RNN, nn.LSTM, nn.GRU, details their parameters, demonstrates code examples for building and testing these models, and provides practical insights for deep learning practitioners.

GRULSTMPyTorch
0 likes · 9 min read
Implementing RNN, LSTM, and GRU with PyTorch
Model Perspective
Model Perspective
Mar 2, 2023 · Artificial Intelligence

Understanding RNNs and LSTM: Theory and Python Keras Implementation

This article explains the fundamentals of Recurrent Neural Networks and Long Short‑Term Memory units, their gating mechanisms, and demonstrates a practical Python Keras example that predicts future PM2.5 concentrations using an LSTM model.

KerasLSTMPython
0 likes · 7 min read
Understanding RNNs and LSTM: Theory and Python Keras Implementation
DataFunSummit
DataFunSummit
Feb 1, 2023 · Artificial Intelligence

Clustering-Based Global LSTM Models for Large-Scale Time Series Forecasting

The paper proposes clustering thousands of related time series and training separate global LSTM models for each cluster, showing that this reduces heterogeneity, leverages shared information, and improves forecasting accuracy compared to individual models, with extensive experiments on CIF2016 and NN5 datasets.

Big DataClusteringLSTM
0 likes · 33 min read
Clustering-Based Global LSTM Models for Large-Scale Time Series Forecasting
Ctrip Technology
Ctrip Technology
Oct 20, 2022 · Artificial Intelligence

Mid‑ and Long‑Term Monthly Hotel Room‑Night Forecasting under Pandemic Conditions

This article presents a pandemic‑aware method for predicting national hotel monthly room‑nights over the next six months, detailing data augmentation, feature engineering, LSTM and SARIMA‑LASSO modeling, scenario‑based risk assessment, and evaluation results that demonstrate accurate forecasts despite COVID‑19 disruptions.

AILSTMSARIMA
0 likes · 14 min read
Mid‑ and Long‑Term Monthly Hotel Room‑Night Forecasting under Pandemic Conditions
Model Perspective
Model Perspective
Oct 10, 2022 · Artificial Intelligence

Predict Air Pollution with Multivariate LSTM in Keras: A Step‑by‑Step Guide

This tutorial explains how to build, train, and evaluate a multivariate LSTM model using Keras for hourly air‑pollution forecasting, covering data preparation, model design, prediction, and inverse scaling back to original units.

KerasLSTMPython
0 likes · 13 min read
Predict Air Pollution with Multivariate LSTM in Keras: A Step‑by‑Step Guide
Model Perspective
Model Perspective
Oct 6, 2022 · Artificial Intelligence

Demystifying RNNs and LSTMs: Architecture, Limits, and Python Forecasting

This article explains the structure and operation of recurrent neural networks (RNNs), their limitations, how long short‑term memory (LSTM) networks overcome these issues with gated mechanisms, and provides a complete Python implementation for time‑series airline passenger forecasting.

LSTMPythonRNN
0 likes · 17 min read
Demystifying RNNs and LSTMs: Architecture, Limits, and Python Forecasting
Model Perspective
Model Perspective
Aug 15, 2022 · Artificial Intelligence

Understanding Recurrent Neural Networks: From Vanilla RNN to LSTM with Keras

This article introduces recurrent neural networks (RNNs) and their ability to handle sequential data, explains the limitations of vanilla RNNs, presents the LSTM architecture with its gates, and provides complete Keras code for data loading, model building, and training both vanilla RNN and LSTM models.

KerasLSTMRNN
0 likes · 5 min read
Understanding Recurrent Neural Networks: From Vanilla RNN to LSTM with Keras
Python Programming Learning Circle
Python Programming Learning Circle
Jul 14, 2022 · Artificial Intelligence

End‑to‑End Time Series Forecasting with LSTM in Python

This tutorial walks through loading Google stock data, preprocessing it with scaling, constructing past‑window features, building and tuning an LSTM model using GridSearchCV, evaluating predictions, and finally forecasting future values, all illustrated with complete Python code.

KerasLSTMPython
0 likes · 14 min read
End‑to‑End Time Series Forecasting with LSTM in Python
DataFunSummit
DataFunSummit
Apr 23, 2022 · Artificial Intelligence

Intelligent Vehicle‑Cargo Matching, Driver Tagging, and Freight Price Prediction in a Logistics Big Data Platform

The article describes how a logistics company built a data‑driven platform that uses big‑data storage, DeepFM and LSTM models, and real‑time GPS tracking to create an intelligent vehicle‑cargo matching system, a multi‑label driver tagging framework, and a freight price prediction engine, thereby improving efficiency and reducing costs across the industry.

Big DataLSTMLogistics
0 likes · 16 min read
Intelligent Vehicle‑Cargo Matching, Driver Tagging, and Freight Price Prediction in a Logistics Big Data Platform
DataFunSummit
DataFunSummit
Oct 15, 2021 · Artificial Intelligence

Risk Control and Operations for Existing Loan Customers

This article examines how financial institutions can manage risk and improve operations for existing loan customers by analyzing customer flow during the pandemic, policy impacts, rapid deterioration patterns, and by applying advanced models such as LSTM, survival analysis, and B‑card strategies to enable timely risk detection and targeted cross‑selling.

LSTMbehavior modelingcustomer operations
0 likes · 19 min read
Risk Control and Operations for Existing Loan Customers
58 Tech
58 Tech
Oct 12, 2021 · Artificial Intelligence

Seq2Seq Approaches for Phone Number Extraction from Two‑Speaker Voice Dialogues

This article presents a practical study of extracting phone numbers from two‑speaker voice dialogues using Seq2Seq models—including LSTM, GRU with attention and feature fusion, and Transformer—detailing data characteristics, model architectures, training strategies, experimental results, and comparative analysis showing the GRU‑Attention approach achieving the best performance.

GRULSTMNLP
0 likes · 13 min read
Seq2Seq Approaches for Phone Number Extraction from Two‑Speaker Voice Dialogues