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Programming with TensorFlow = soluti...

Prakash, Kolla Bhanu.

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Programming with TensorFlow = solution for edge computing applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Programming with TensorFlow/ edited by Kolla Bhanu Prakash, G. R. Kanagachidambaresan.
Reminder of title:	solution for edge computing applications /
other author:	Prakash, Kolla Bhanu.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	x, 190 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Installation Guide to Tensorflow -- Hello Tensorflow Program -- Representation of Vector -- Session with Tensorflow -- Matrix elementary operation -- Variable and constant -- Simple mathematical operation -- Matrix -- Variable Concept & Implementation -- Placeholder Concept & Implementation -- Equation with Tensor -- Matplot -- Regression Model -- Neural Network -- Convolutional Neural Network -- Recurrent Neural Network -- Application of Machine Learning & Deep Learning -- Implementing Chatbots -- Working with Text and Sequences + TensorBoard visualization -- TensorFlow Autoencoders -- Advanced TensorFlow Programming -- Reinforcement Learning -- RNN & LSTM using Keras -- Deep Learning with Pytorch -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science) -
Online resource:	https://doi.org/10.1007/978-3-030-57077-4
ISBN:	9783030570774

Programming with TensorFlow = solution for edge computing applications /
Programming with TensorFlowsolution for edge computing applications /[electronic resource] :edited by Kolla Bhanu Prakash, G. R. Kanagachidambaresan. - Cham :Springer International Publishing :2021. - x, 190 p. :ill., digital ;24 cm. - EAI/Springer innovations in communication and computing,2522-8595. - EAI/Springer innovations in communication and computing..

Introduction -- Installation Guide to Tensorflow -- Hello Tensorflow Program -- Representation of Vector -- Session with Tensorflow -- Matrix elementary operation -- Variable and constant -- Simple mathematical operation -- Matrix -- Variable Concept & Implementation -- Placeholder Concept & Implementation -- Equation with Tensor -- Matplot -- Regression Model -- Neural Network -- Convolutional Neural Network -- Recurrent Neural Network -- Application of Machine Learning & Deep Learning -- Implementing Chatbots -- Working with Text and Sequences + TensorBoard visualization -- TensorFlow Autoencoders -- Advanced TensorFlow Programming -- Reinforcement Learning -- RNN & LSTM using Keras -- Deep Learning with Pytorch -- Conclusion.

This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience--from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs) Provides a practical end-to-end guide to TensorFlow, the leading open source software library for building and training neural networks; Pertains to a broad technical audience--from data scientists and engineers to students and researchers; Shows how to implement advanced techniques in deep learning and explore deep neural networks and layers of data abstraction.

ISBN: 9783030570774

Standard No.: 10.1007/978-3-030-57077-4doiSubjects--Uniform Titles:

TensorFlow.
Subjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87 / .P764 2021

Dewey Class. No.: 006.32

Programming with TensorFlow = solution for edge computing applications /
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245 0 0 $a Programming with TensorFlow $h [electronic resource] : $b solution for edge computing applications / $c edited by Kolla Bhanu Prakash, G. R. Kanagachidambaresan.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a x, 190 p. : $b ill., digital ; $c 24 cm.
490 1 $a EAI/Springer innovations in communication and computing, $x 2522-8595
505 0 $a Introduction -- Installation Guide to Tensorflow -- Hello Tensorflow Program -- Representation of Vector -- Session with Tensorflow -- Matrix elementary operation -- Variable and constant -- Simple mathematical operation -- Matrix -- Variable Concept & Implementation -- Placeholder Concept & Implementation -- Equation with Tensor -- Matplot -- Regression Model -- Neural Network -- Convolutional Neural Network -- Recurrent Neural Network -- Application of Machine Learning & Deep Learning -- Implementing Chatbots -- Working with Text and Sequences + TensorBoard visualization -- TensorFlow Autoencoders -- Advanced TensorFlow Programming -- Reinforcement Learning -- RNN & LSTM using Keras -- Deep Learning with Pytorch -- Conclusion.
520 $a This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience--from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs) Provides a practical end-to-end guide to TensorFlow, the leading open source software library for building and training neural networks; Pertains to a broad technical audience--from data scientists and engineers to students and researchers; Shows how to implement advanced techniques in deep learning and explore deep neural networks and layers of data abstraction.
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650 0 $a Machine learning. $3 533906
650 0 $a Python (Computer program language) $3 729789
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650 0 $a Image processing. $3 621117
650 0 $a Speech processing systems. $3 590570
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650 1 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Programming Languages, Compilers, Interpreters. $3 891123
700 1 $a Prakash, Kolla Bhanu. $3 3489825
700 1 $a Kanagachidambaresan, G. R. $3 3446282
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
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856 4 0 $u https://doi.org/10.1007/978-3-030-57077-4
950 $a Engineering (SpringerNature-11647)