Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications, that range from speech recognition to face-based user identification. Despite other ...

Restricted Boltzmann Machines (RBMs) are among the most widely pursed techniques in the context of deep learning-based applications. Their usage enables sundry parallel implementations, which have become pivotal in nowadays ...

The Deep learning framework has been widely used in different applications from medicine to engineering. However, there is a lack of works that manage to deal with the issue of hyperparameter fine-tuning, since machine ...

Big Data presents considerable challenges to deep learning for transforming complex, high-dimensional, and heterogeneous biomedical data into health care data. Various kinds of data are analyzed in recent biomedical research ...

The Deep Learning models have drawn ever-increasing research interest owing to their intrinsic capability of overcoming the drawback of traditional algorithm. Hence, we have adopted the representative Deep Learning methods ...

Deep Learning is a Machine Learning’s sub-area that have achieved satisfactory results in different application areas, implemented by different algorithms, such as Stacked Auto- encoders or Deep Belief Networks. This work ...