In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.
Mini-batch Gradient Descent: In this approach, the training samples are partitioned into several mini-batches, in which every mini-batch can be considered an under-sized collection of samples with no overlap between them [84]. Next, parameter updating is performed following gradient computation on every mini-batch. The advantage of this method comes from combining the advantages of both BGD and SGD techniques. Thus, it has a steady convergence, more computational efficiency and extra memory effectiveness. The following describes several enhancement techniques in gradient-based learning algorithms (usually in SGD), which further powerfully enhance the CNN training process.
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Several common assumptions are made when forming pairs or social groups of NHPs. First, the process of pairings or groupings will be relatively straightforward, and there are reliable ways to test for compatibility in advance of pairing. Second, if pairs or social groups are compatible in the beginning, they will remain compatible throughout. Finally, if incompatibility develops, there will be significant changes in behavior that will enable separation of such pairs prior to injury. However, the data do not fully support these assumptions. First, pairing success can vary widely. In one study of vervet monkeys, pairing success ranged from 20% to 98% across four facilities. This variability was attributed to differences in pairing procedure and whether monkeys were laboratory born or imported (Jorgensen et al. 2017). Additionally, there are many different factors to consider that may influence partner compatibility, including, but not limited to, sex, age, body weight, rearing history, temperament, and previous pairing history. Temperament may be one of the more salient dimensions to use for compatibility. In one study, temperamental characteristics as determined in infancy predicted successful pairings in adulthood for female pairs but not for male pairs (Capitanio et al. 2017). However, there is no blueprint at present that can be used reliably to ensure pairing success in most situations. 2ff7e9595c
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