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Download Free PDFCÀI TENSORFLOW KERASThanh NguyenTutorial on setup Python 7.3, OpenCV, TensorFlow Anaconda on Windoows Operating System
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downloadDownload free PDFView PDFchevron_rightPROGRAMMING WITH PYTHON ON UBUNTU Part Build the Environment eJournal emerging-mind lab (EML) ISSN-info@emerging-mind.orgGerd Doeben-HenischAfter the description of how to use the python programming language under windows starting from scratch in posts we start here with some more posts dedicated to the question how to start programming with python under linux using the distribution ubuntu .. This distribution is selected because we will use later the ros (robotic operating system) as well as the tensorflow library.
downloadDownload free PDFView PDFchevron_rightReview on Different Software Tools for Deep LearningIJRASET PublicationInternational Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Deep Learning Applications are being applied in various domains in recent years. Training a deep learning model is a very time consuming task. But, many open source frameworks are available to simplify this task. In this review paper we have discussed the features of some popular open source software tools available for deep learning along with their advantages and disadvantages. Software tools discussed in this paper are Tensorflow, Keras, Pytorch, Microsoft Cognitive Toolkit (CNTK).
downloadDownload free PDFView PDFchevron_rightTENSORFLOW 2.0 AND KUBEFLOW FOR SCALABLE AND REPRODUCABLE ENTERPRISE AIComputer Science & Information Technology (CS & IT) Computer Science Conference Proceedings (CSCP)Towards the End of 2015 Google released TensorFlow 1.0, which started out as just another numerical library, but has grown to become a de-facto standard in AI technologies. TensorFlow received a lot of hype as part of its initial release, in no small part because it was released by Google. Despite the hype, there have been complaints on usability as well. Especially, for example, the fact that debugging was only possible after construction of a static execution graph. In addition to that, neural networks needed to be expressed as a set of linear algebra operations which was considered as too low level by many practitioners. PyTorch and Keras addressed many of the flaws in TensorFlow and gained a lot of ground. TensorFlow 2.0 successfully addresses these complaints and promises to become the go-to framework for many AI problems. This paper introduces the most prominent changes in TensorFlow 2.0 targeted towards ease of use followed by introducing TensorFlow Extended Pipelines and KubeFlow in order to illustrate the latest TensorFlow and Kubernetes ecosystem movements towards simplification for large scale Enterprise AI adoption.
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