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Hands-on neural networks with TensorFlow 2.0 : understand TensorFlow, from static graph to eager execution, and design neural networks

Hands-on neural networks with TensorFlow 2.0 : understand TensorFlow, from static graph to eager execution, and design neural networks (Loan 4 times)

Material type
단행본
Personal Author
Galeone, Paolo.
Title Statement
Hands-on neural networks with TensorFlow 2.0 : understand TensorFlow, from static graph to eager execution, and design neural networks / Paolo Galeone.
Publication, Distribution, etc
Birmingham, UK :   Packt,   2019.  
Physical Medium
v, 344 p. : ill. ; 24 cm.
ISBN
9781789615555
General Note
Includes index.  
Subject Added Entry-Topical Term
Neural networks (Computer science). Machine learning.
000 00000nam u2200205 a 4500
001 000046013248
005 20200115112742
008 200115s2019 enka 001 0 eng d
020 ▼a 9781789615555
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 006.32 ▼2 23
084 ▼a 006.32 ▼2 DDCK
090 ▼a 006.32 ▼b G153h
100 1 ▼a Galeone, Paolo.
245 1 0 ▼a Hands-on neural networks with TensorFlow 2.0 : ▼b understand TensorFlow, from static graph to eager execution, and design neural networks / ▼c Paolo Galeone.
260 ▼a Birmingham, UK : ▼b Packt, ▼c 2019.
300 ▼a v, 344 p. : ▼b ill. ; ▼c 24 cm.
500 ▼a Includes index.
630 0 0 ▼a TensorFlow.
650 0 ▼a Neural networks (Computer science).
650 0 ▼a Machine learning.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.32 G153h Accession No. 121251891 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

Table of Contents

What is Machine Learning?
Neural Networks and Deep Learning
TensorFlow Graph Architecture
TensorFlow 2.0 Architecture
Efficient Data Input Pipelines and Estimator API
Image Classification using TensorFlow Hub
Introduction to Object Detection
Semantic Segmentation and Custom Dataset Builder
Generative Adversarial Networks
Bringing a Model to Production

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