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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.

AttentionLSTMNLP
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 19, 2023 · Artificial Intelligence

NLP Basics: Word Embeddings, Word2Vec, and Hand‑crafted RNN Implementation in PyTorch

This article introduces word‑level representations—from one‑hot encoding to dense word embeddings via Word2Vec—explains cosine similarity, then walks through the structure, limitations, and PyTorch implementation of a vanilla RNN, including a custom forward function and verification against the library API.

NLPPyTorchRNN
0 likes · 19 min read
NLP Basics: Word Embeddings, Word2Vec, and Hand‑crafted RNN Implementation in PyTorch
Efficient Ops
Efficient Ops
Sep 12, 2023 · Artificial Intelligence

AI-Powered Text Clustering and RNNs Automate Test Environment Issue Diagnosis

This article describes how a Chinese bank’s software development team leveraged AI techniques—text clustering and recurrent neural networks—to automatically classify and diagnose test-environment problems, dramatically reducing manual effort, improving issue visibility, and enabling self-healing mechanisms for faster, more reliable software delivery.

AIRNNissue classification
0 likes · 5 min read
AI-Powered Text Clustering and RNNs Automate Test Environment Issue Diagnosis
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.

KerasLSTMRNN
0 likes · 9 min read
Mastering LSTM: How Long Short-Term Memory Networks Capture Long-Term Dependencies
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 31, 2023 · Artificial Intelligence

Overview of Deep Neural Network Architectures

This article provides a comprehensive overview of deep neural network families, introducing twelve major architectures—including Feedforward, CNN, RNN, LSTM, DBN, GAN, Autoencoder, Residual, Capsule, Transformer, Attention, and Deep Reinforcement Learning—explaining their principles, structures, training methods, and offering Python/TensorFlow/PyTorch code examples.

CNNGANNeural Networks
0 likes · 29 min read
Overview of Deep Neural Network Architectures
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.

GRULSTMNeural Networks
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.

KerasLSTMRNN
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.

LSTMMachine LearningRNN
0 likes · 33 min read
Clustering-Based Global LSTM Models for Large-Scale Time Series Forecasting
Model Perspective
Model Perspective
Jan 12, 2023 · Artificial Intelligence

Neural Networks Explained: Architecture, Training, and Reinforcement Basics

This article introduces neural networks, covering their layered structure, common types like CNNs and RNNs, key components such as activation functions, loss, learning rate, backpropagation, dropout, batch normalization, and extends to reinforcement learning concepts including MDPs, policies, value functions, and Q‑learning.

CNNMachine LearningNeural Networks
0 likes · 6 min read
Neural Networks Explained: Architecture, Training, and Reinforcement Basics
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.

LSTMNeural NetworksRNN
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
DataFunSummit
DataFunSummit
Nov 21, 2021 · Artificial Intelligence

Sequential Recommendation Algorithms: Overview and Techniques

This article surveys sequential recommendation methods, covering standard models such as pooling, RNN, CNN, attention, and Transformer, as well as long‑short term, multi‑interest, multi‑behavior approaches, and recent advances like contrastive learning, highlighting their impact on recommendation performance.

AttentionMachine LearningRNN
0 likes · 8 min read
Sequential Recommendation Algorithms: Overview and Techniques
DataFunSummit
DataFunSummit
Dec 27, 2020 · Artificial Intelligence

Sequence Labeling in Natural Language Processing: Definitions, Tag Schemes, Model Choices, and Practical Implementation

This article provides a comprehensive overview of sequence labeling tasks in NLP, covering their definition, common tag schemes (BIO, BIEO, BIESO), comparisons with other NLP tasks, major modeling approaches such as HMM, CRF, RNN and BERT, real‑world applications like POS tagging, NER, event extraction and gene analysis, and a step‑by‑step PyTorch implementation with dataset preparation, training pipeline, and evaluation metrics.

BERTCRFHMM
0 likes · 27 min read
Sequence Labeling in Natural Language Processing: Definitions, Tag Schemes, Model Choices, and Practical Implementation
Sohu Tech Products
Sohu Tech Products
Nov 18, 2020 · Artificial Intelligence

Understanding Sequence‑to‑Sequence (seq2seq) Models and Attention Mechanisms

This article explains the fundamentals of seq2seq neural machine translation models, covering encoder‑decoder architecture, word embeddings, context vectors, RNN processing, and the attention mechanism introduced by Bahdanau and Luong, with visual illustrations and reference links for deeper study.

AttentionRNNdeep learning
0 likes · 11 min read
Understanding Sequence‑to‑Sequence (seq2seq) Models and Attention Mechanisms
Architects' Tech Alliance
Architects' Tech Alliance
Sep 3, 2020 · Artificial Intelligence

Deep Learning Specialization Infographic Overview

This article presents a comprehensive English summary of the deep learning specialization infographics originally shared by Andrew Ng, covering fundamentals, logistic regression, shallow and deep neural networks, regularization, optimization, hyperparameters, convolutional and recurrent networks, and practical advice for model building and evaluation.

CNNNeural NetworksRNN
0 likes · 21 min read
Deep Learning Specialization Infographic Overview
DataFunTalk
DataFunTalk
Jun 22, 2020 · Artificial Intelligence

Ctrip's Automated Iterative Anti‑Fraud Modeling Framework for Payment Risk

The article describes Ctrip's payment fraud risk characteristics, a comprehensive automated iterative anti‑fraud model framework—including variable system, GAN‑augmented sample generation, RNN behavior encoding, and tree‑based classifiers—and demonstrates how this approach restores recall performance compared with traditional static models.

GANMachine LearningRNN
0 likes · 12 min read
Ctrip's Automated Iterative Anti‑Fraud Modeling Framework for Payment Risk
DataFunTalk
DataFunTalk
Jun 13, 2020 · Artificial Intelligence

Deep Learning for Expired POI Detection at Amap: Feature Engineering, RNN, Wide&Deep, and Attention‑TCN

This article details how Amap leverages deep‑learning techniques—including temporal and auxiliary feature engineering, multi‑stage RNN models, Wide&Deep architectures, and an Attention‑TCN approach—to accurately identify and handle expired points of interest, improving map freshness and user experience.

POI expirationRNNTCN
0 likes · 13 min read
Deep Learning for Expired POI Detection at Amap: Feature Engineering, RNN, Wide&Deep, and Attention‑TCN
Amap Tech
Amap Tech
May 8, 2020 · Artificial Intelligence

Expired POI Detection in Amap Using Deep Learning: Feature Engineering, RNN, Wide&Deep, and TCN Models

The project develops a deep‑learning pipeline for Amap’s expired POI detection that integrates two‑year temporal trend features, industry and verification attributes, a variable‑length LSTM, a Wide‑Deep architecture, and a Wide‑Attention Temporal Convolutional Network, achieving higher accuracy and efficiency while outlining future macro‑and micro‑level enhancements.

POI expirationRNNTCN
0 likes · 15 min read
Expired POI Detection in Amap Using Deep Learning: Feature Engineering, RNN, Wide&Deep, and TCN Models