… In this paper, we are comparing some of the deep learning frameworks on the basis of
parameters like modeling capability, interfaces available, platforms supported, parallelizing …

… DL frameworks and libraries. It is divided into three subsections: Machine Learning frameworks
… 4.1), Deep Learning frameworks and libraries with GPU support (Sect. 4.2), and Machine …

Deep Learning (DL) is one of the hottest trends in machine learning as DL approaches produced
… To foster the growth of the DL community, several open source frameworks appeared …

… frameworks have been developed to facilitate their implementation. This paper presents a
comparative study of five deep learning frameworks, … types of deep learning architectures and …

… is the recent deep learning CD … deep learning frameworks for change detection. Particularly,
the existing CD methods are analyzed in terms of model design and evaluation frameworks. …

… Timeline showing the date of occurrence of the DL frameworks … DL frameworks information
briefly is provided. And highlighted the advantages and disadvantages of these frameworks …

… the door to new ways of testing hypotheses derived from the deep learning literature. Here
we sketch the scaffolding of a deep learning framework for modern systems neuroscience. …

… deep learning frameworks, namely, Tensorflow, PyTorch and Theano. Within each platform,
we compare the topics across the three deep learning frameworks… deep learning frameworks …

… Section 3 provides an overview of the different deep learning frameworks that have been
considered in this study. Section 4 describes the details of our experimental setup in terms of …

… of deep learning for educational data mining and learning analytics. We compare the
predictive accuracy of popular deep learning frameworks/… Moreover, we find that deep learning …