Abstract
Developed by Google Brain team, tensor flow in an open source library package originally created for the tasks in heavy numerical computations (Learning TensorFlow [Authors: Tom Hope, Yehezkel S. Resheff & Itay Lieder]). Its main application is machine learning and deep learning where a computer learns from experience and the world is understood in the form of hierarchy of concepts, each concept defining its relation to simpler concepts (Deep Learning Pipeline: Building A Deep Learning Model With TensorFlow [Authors: Hisham El-Amir, Mahmoud Hamdy]). Drawing the hierarchy graph built on top of each other, the graph is deep with many layers and computation costly. Tensorflow provides faster computation and an excellent functionality compared to other popular deep learning frameworks/library. It also supports Central Processing Unit (CPUs), Graphics Processing Unit (GPUs) and distributed processing in a cluster.
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References
Learning TensorFlow [Authors: Tom Hope, Yehezkel S. Resheff & Itay Lieder]
Deep Learning Pipeline: Building A Deep Learning Model With TensorFlow [Authors: Hisham El-Amir, Mahmoud Hamdy]
TensorFlow for Machine Intelligence_ A Hands-On Introduction to Learning Algorithms [Authors: Sam Abrahams, Danijar Hafner, Erik Erwitt, Ariel Scarpinelli]
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow [Authors: Sebastian Raschka, Vahid Mirjalili]
Python Deep Learning: Exploring deep learning techniques, neural network architectures and GANs with PyTorch, Keras and TensorFlow. [Authors: Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca]
S. Pichai, “TensorFlow: smarter machine learning for everyone”, Google Official Blog, 2015.
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Prakash, K.B., Ruwali, A., Kanagachidambaresan, G.R. (2021). Introduction to Tensorflow Package. In: Prakash, K.B., Kanagachidambaresan, G.R. (eds) Programming with TensorFlow. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-57077-4_1
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