Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.

翻译：深度学习（DL）能够开发具备学习、可视化、优化、精炼及预测数据能力的计算机模型。近年来，该技术已广泛应用于视听数据处理、农业、交通预测、自然语言处理、生物医学、灾害管理、生物信息学、药物设计、基因组学、人脸识别及生态学等领域。为把握深度学习发展现状，需系统探究其在各学科中的最新进展与应用。现有文献尚未系统梳理深度学习在所有潜在领域的应用情况。本文因此全面考察了深度学习在各主要研究领域的潜在应用、相关优势与挑战。文献表明，DL在预测与分析方面展现出高准确性，成为强大的计算工具；其具备自表述与自优化能力，无需预训练即可高效处理数据。尽管不依赖训练数据，但深度学习仍需海量数据以实现有效分析与处理——这与数据规模需求密切相关。为应对医学、科学、医疗及环境领域庞大数据集的整合挑战，可采用LSTM、GRU等门控架构。对于多模态学习，神经网络需同时设置共享神经元处理通用任务与专用神经元处理特定任务。