ABSTRACT
In recent times, machine learning (ML) algorithms have been used extensively to interpret the data and finding significantly correlated information in them, which can be applied to other useful applications. An explosive growth has been witnessed in the dimension and structure of data. To deal with the voluminous and unstructured data, numerous snags are encountered by conventional ML algorithms with their conventional architectures and functions. Modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which is commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This study focuses some of the prominent architectures of DL along with the open sources tools and software which are in current in various areas and especially in the area of healthcare applications. The investigation is carried out by considering the popular open source tools and libraries which are being used for scientific research, development and academic work. Future research areas are identified in of this study.
