—The subject of deep learning has emerged in the last decade as one of the most promising approaches to ma- chine learning. Today, certainly, much of the recent progress in artiﬁcial intelligence is due to it, but research challenges are still unresolved and remain open to the research community. This paper attempts to offer a comprehensive review of deep learning progress in active research frontiers. On the one side, by presenting a brief overview of deep learning success, we inspire researchers to work in deep learning. On the other hand, we examine a range of technical issues, and open research issues that we believe are relevant topics for exploratory research. As deep learning applies to various ﬁelds, we restrict this paper’s scope to visual recognition tasks to analyse these problems with a speciﬁc lens. However, these problems will be broadly applicable to other ﬁelds. It will make it easier for new researchers to recognise outstanding research problems in the deep learning domain. Index Terms —Deep Learning, Artiﬁcial Intelligence, Self- supervised learning, Contrastive Learning, Meta-Learning, Ader- serial Learning. I. I NTRODUCTION Deep learning refers to a broad range of machine learning techniques that learn from large volumes of data. Deep learn- ing algorithms are basically artiﬁcial neural networks that learn from data repeatedly, ﬁne-tuning the task a little more each time. We call neural networks ”deep learning” because they have many layers that allow for complex learning. The term ”deep learning” refers to the number of layers from which data is processed [1]. A problem can be described scientiﬁcally as a general issue, concern, or controversy addressed in research. Additionally, to successfully conduct research, a problem must bring together existing concepts and theoretical perspectives to solve it. A research problem does not pose a vague or open-ended proposition, nor does it address a value issue. Therefore, researchers or students should focus on the problems to be solved, and the conditions to be improved, or the challenges to be overcome. “I keep six honest serving-men (They taught me all I knew); Their names are What and Why and When And How and Where and Who” [2]. Many disciplines have used “honest serving-men” as the foundation for an investigation. However, this verse has been the most commonly quoted axiom of journalists since time immemorial, and it applies equally well to scientiﬁc research [3].My ultimate goal for writing this paper is to help the reader understand the criticality of deep learning research so that the reader is prepared for what is about to happen and what will be discovered in the subsequent research questions or hypotheses. I want to deﬁne the parameters of what is to be investigated in deep learning for the long run. I have deﬁned the following problems in deep learning after an extensive literature survey. 1) Performance ( Accuracy vs Complexity Trade-off) 2) Scalability ( Distributed Deep Learning, Deep learning on Cloud) 3) Optimisation (Parameter and Hyperparameter Optimisa- tion, AutoML, Neural Architecture Search) 4) Generalisation (Regularisation, Domain Adaptation and Meta-Learning) 5) Data-Efﬁcient Learning (Data Augmentation and self- supervised learning), 6) Interpretability and Explainability (Feature Visualisation and Explainable AI) 7) Security (Robustness, Safety and Reliability) 8) Fairness and Ethics (Privacy, Accountability, Trans- parency, Federated Learning and, Privacy by design ) 9) Artiﬁcial Creativity (Genarative Models and Reinforce- ment Learning) 10) Artiﬁcial General Intelligence (Emotional intelligence, life loss) This review paper is organised as follows: In section 2, we provide a list of top ten active research problem areas in deep learning . Section 3 presents the discussion and future research directions followed by a conclusion and