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.

翻译：深度学习作为机器学习与人工智能的重要分支，在处理非结构化及大规模数据集方面展现出显著优势，其性能已超越传统机器学习方法。该技术的影响范围涵盖语音识别、医疗健康、自动驾驶、网络安全、预测分析等多个领域。然而，真实世界问题的复杂性与动态特性给有效深度学习模型的设计带来了挑战。为此，研究人员开发了多种深度学习模型以应对不同问题与应用场景。本文对多种深度学习模型进行系统性综述，涵盖卷积神经网络（CNN）、循环神经网络（RNN）、生成模型、深度强化学习（DRL）及深度迁移学习。我们深入剖析了各类模型的结构特性、应用场景、优势与局限性。此外，基于IMDB、ARAS及Fruit-360三个公开数据集，对六种主流深度学习模型（包括CNN、简单RNN、长短期记忆网络（LSTM）、双向LSTM、门控循环单元（GRU）及双向GRU）进行了性能对比分析。