Deep learning for big data analytics is a major research area focused on developing neural network-based techniques to process, analyze, and extract insights from large-scale, high-dimensional, and heterogeneous datasets. Research papers in this domain explore applications across healthcare, finance, IoT, social media, smart cities, cybersecurity, and industrial systems. Key deep learning models include convolutional neural networks (CNNs) for structured and unstructured data, recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) for temporal and sequential data, autoencoders for dimensionality reduction and anomaly detection, and transformer-based models for multi-modal data analysis. Contributions also include scalable architectures, distributed and parallel training frameworks, data preprocessing and feature extraction techniques, and integration with cloud, edge, and fog computing platforms. Recent studies address challenges such as data heterogeneity, high dimensionality, missing or noisy data, real-time analytics, and energy-efficient computation. By leveraging deep learning, research in big data analytics aims to provide accurate, adaptive, and high-performance solutions for predictive modeling, decision-making, and knowledge discovery in complex real-world scenarios.