… learning models from complex data. In this article, we review the recent literature on applying
deep learning … Based on the analyzed work, we suggest that deep learning approaches …

… automatically, the main challenge in deep learning is that it … learning to deep learning to
overcome this challenge. Most … ensemble learning, especially in the case of deep learning. …

… This paper summarizes existing deep learning research using ECG data from multiple
perspectives and highlights existing challenges and problems to identify potential future research …

… We also highlight ongoing research and identify open challenges in building deep learning
… Third, we discuss special challenges rising from modeling EHR data with deep learning, and …

… Machine learning (ML) has recently regained attention because of the successful applications
of deep learning (DL) in computer vision (CV), automatic speech recognition (ASR), and …

… In addition we discuss the challenges faced by most of the recent algorithms presenting future
research … In section 5 we discuss current challenges and opportunities in robust defenses. …

… opportunities to facilitate the transition toward Industry 4.0–based production. Machine learning
and deep learning … This paper reviews the opportunities and challenges of deep learning …

… of choice for many machine learning tasks. In this review, we address the opportunities that
deep spiking networks offer and investigate in detail the challenges associated with training …

… emergence of deep learning (DL) technology has resulted in its successful use in various
fields, including aquaculture. DL creates both new opportunities and a series of challenges for …

… Deep learning has become an extremely popular method in … The application of deep
learning in disease diagnosis has … of skin disease using deep learning to summarize the …