Deep learning and big data analytics are two key areas in data science. Big data has become important as many organizations collect massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies like Google and Microsoft analyze large volumes of data for business analysis and decisions, impacting existing and future technology. Deep learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. A key benefit of deep learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for big data analytics where raw data is largely unlabeled and un-categorized. This study explores how deep learning can be utilized for addressing important problems in big data analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. It also investigates aspects of deep learning research that need further exploration to incorporate specific challenges introduced by big data analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. The paper focuses on two key topics: (1) how deep learning can assist with specific problems in big data analytics, and (2) how specific areas of deep learning can be improved to reflect certain challenges associated with big data analytics. The paper concludes by identifying important future areas needing innovation in deep learning for big data analytics, including data sampling for generating useful high-level abstractions, domain adaptation, defining criteria for extracting good data representations, semi-supervised learning, and active learning. Deep learning algorithms are promising for automated extraction of complex data representations. They use a hierarchical architecture where higher-level features are defined in terms of lower-level features. Deep learning is beneficial for learning from large amounts of unsupervised data and typically learns data representations in a greedy layer-wise fashion. Deep learning solutions have yielded outstanding results in various machine learning applications, including speech recognition, computer vision, and natural language processing. Big data analytics involves mining and extracting meaningful patterns from massive input data for decision-making, prediction, and other inferencing. It poses unique challenges for machine learning and data analysis, including format variation of the raw data, fast-moving streaming data, trustworthiness of the data analysis, highly distributed input sources, noisy and poor quality data, high dimensionality, scalability of algorithms, imbalanced input data, unsupervised and un-categorized data, limited supervised/labeled data, etc. Adequate data storage, data indexing/tagging, and fast information retrieval are other key problems in big data analytics. Deep learning algorithms can be used for semantic indexing, data tagging, fast information retrieval, and discriminative modeling. Deep learning provides complex representations of data suitable for AI tasks and makes machines independent of human knowledge. Deep learning algorithms use distributed representations of the data, allowing for a compact representation of each sample and leading to a richer generalization. Deep learning algorithms can extract invariant features and disentangleDeep learning and big data analytics are two key areas in data science. Big data has become important as many organizations collect massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies like Google and Microsoft analyze large volumes of data for business analysis and decisions, impacting existing and future technology. Deep learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. A key benefit of deep learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for big data analytics where raw data is largely unlabeled and un-categorized. This study explores how deep learning can be utilized for addressing important problems in big data analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. It also investigates aspects of deep learning research that need further exploration to incorporate specific challenges introduced by big data analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. The paper focuses on two key topics: (1) how deep learning can assist with specific problems in big data analytics, and (2) how specific areas of deep learning can be improved to reflect certain challenges associated with big data analytics. The paper concludes by identifying important future areas needing innovation in deep learning for big data analytics, including data sampling for generating useful high-level abstractions, domain adaptation, defining criteria for extracting good data representations, semi-supervised learning, and active learning. Deep learning algorithms are promising for automated extraction of complex data representations. They use a hierarchical architecture where higher-level features are defined in terms of lower-level features. Deep learning is beneficial for learning from large amounts of unsupervised data and typically learns data representations in a greedy layer-wise fashion. Deep learning solutions have yielded outstanding results in various machine learning applications, including speech recognition, computer vision, and natural language processing. Big data analytics involves mining and extracting meaningful patterns from massive input data for decision-making, prediction, and other inferencing. It poses unique challenges for machine learning and data analysis, including format variation of the raw data, fast-moving streaming data, trustworthiness of the data analysis, highly distributed input sources, noisy and poor quality data, high dimensionality, scalability of algorithms, imbalanced input data, unsupervised and un-categorized data, limited supervised/labeled data, etc. Adequate data storage, data indexing/tagging, and fast information retrieval are other key problems in big data analytics. Deep learning algorithms can be used for semantic indexing, data tagging, fast information retrieval, and discriminative modeling. Deep learning provides complex representations of data suitable for AI tasks and makes machines independent of human knowledge. Deep learning algorithms use distributed representations of the data, allowing for a compact representation of each sample and leading to a richer generalization. Deep learning algorithms can extract invariant features and disentangle