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GRU

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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.

Deep LearningGRULSTM
0 likes · 9 min read
Implementing RNN, LSTM, and GRU with PyTorch
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.

AttentionGRULSTM
0 likes · 13 min read
Seq2Seq Approaches for Phone Number Extraction from Two‑Speaker Voice Dialogues
DataFunTalk
DataFunTalk
Dec 13, 2019 · Artificial Intelligence

Fundamentals of Deep Learning: Neural Networks, CNNs, RNNs, LSTM, and GRU

This article provides a comprehensive overview of deep learning fundamentals, covering neural network basics, forward and backward feedback architectures, key models such as MLP, CNN, RNN, LSTM and GRU, training techniques like gradient descent, learning rate schedules, momentum, weight decay, and batch normalization.

CNNDeep LearningGRU
0 likes · 14 min read
Fundamentals of Deep Learning: Neural Networks, CNNs, RNNs, LSTM, and GRU
Xianyu Technology
Xianyu Technology
May 10, 2018 · Artificial Intelligence

Mercari Price Prediction Using TFIDF, GRU, and Ensemble Models

By converting Mercari’s product titles, descriptions, and categorical data into TF‑IDF vectors and embeddings, training MLP and GRU networks, and ensembling them with weighted averaging, the authors achieve a 0.3873 RMSLE—matching the competition’s top score—and demonstrate the power of text‑only price prediction for C2C marketplaces.

GRUMachine LearningTFIDF
0 likes · 8 min read
Mercari Price Prediction Using TFIDF, GRU, and Ensemble Models
Liulishuo Tech Team
Liulishuo Tech Team
Aug 11, 2017 · Artificial Intelligence

DeepGrammar: A Neural Network Approach for Grammatical Error Detection and Correction

DeepGrammar is a bidirectional GRU‑based neural model that detects subject‑verb agreement errors by encoding surrounding context into fixed‑length vectors, outperforming rule‑based, classifier, and NMT approaches on the CoNLL‑2014 benchmark and achieving state‑of‑the‑art results across multiple error types.

Deep LearningGRUNeural Networks
0 likes · 8 min read
DeepGrammar: A Neural Network Approach for Grammatical Error Detection and Correction