5,719 research outputs found
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU
Deep learning (DL) has emerged as a powerful subset of machine learning (ML)
and artificial intelligence (AI), outperforming traditional ML methods,
especially in handling unstructured and large datasets. Its impact spans across
various domains, including speech recognition, healthcare, autonomous vehicles,
cybersecurity, predictive analytics, and more. However, the complexity and
dynamic nature of real-world problems present challenges in designing effective
deep learning models. Consequently, several deep learning models have been
developed to address different problems and applications. In this article, we
conduct a comprehensive survey of various deep learning models, including
Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs),
Generative Models, Deep Reinforcement Learning (DRL), and Deep Transfer
Learning. We examine the structure, applications, benefits, and limitations of
each model. Furthermore, we perform an analysis using three publicly available
datasets: IMDB, ARAS, and Fruit-360. We compare the performance of six renowned
deep learning models: CNN, Simple RNN, Long Short-Term Memory (LSTM),
Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU.Comment: 16 pages, 29 figure
- …