With the development of internet technology, big data analysis has become essential. Many organizations are collecting large amounts of information and analyzing it to get valuable information, such as business forecasts, marketing strategies, network security, and even national intelligence. Because deep learning can analyze large amounts of unsupervised data, it is an extremely promising technology for big data analytics in several key application areas.

With the rapid growth of data in social networks, biological networks, the Internet of Things, and other mobile computing applications, current big data is characterized by unstructured heterogeneity, large quantity, and complexity. These new characteristics present great challenges to existing techniques of data analytics, and especially for deep learning. Thus, deep learning will play an important role in meeting the challenges of big data and providing effective big data analysis solutions.

The aim of this Special Issue is to provide a forum for researchers and practitioners to exchange ideas and progress in this area. In this Special Issue, we invite innovative original research and review articles to address the challenges of Deep Learning for Big Data Analytics (CDLBDA). Articles presenting reviews, perspectives, new methods, and applications of CDLBDA are cordially invited.

Potential topics include but are not limited to the following:

• Deep learning for big data analysis
• Deep Learning for unstructured big data
• Deep learning for heterogeneous big data
• Deep learning for multi-source big data
• Deep learning for large quantity and complexity big data
• Deep learning for different types of big data analysis
• Big data analytics and associated issues and challenges
• Big data analytics for Internet of Things
• Big data analytics for biological networks
• Big data analytics for social networks
• Big data analytics solutions for data-driven decision making
• Big data and risk management
• Big data analytics for fraud detection
• Security issue for deep learning and big data
• Cloud computing, big data analytics models, and paradigms