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عنوان
Practical computer vision applications using deep learning with CNNs :

پدید آورنده
Ahmed Fawzy Gad.

موضوع
Computer programming.,Computer vision.,Python (Computer program language),Computer programming.,Computer vision.,Python (Computer program language)

رده
Q337
.
5
.
G33
2018

کتابخانه
Center and Library of Islamic Studies in European Languages

محل استقرار
استان: Qom ـ شهر: Qom

Center and Library of Islamic Studies in European Languages

تماس با کتابخانه : 32910706-025

INTERNATIONAL STANDARD BOOK NUMBER

(Number (ISBN
1484241673
(Number (ISBN
9781484241677
Erroneous ISBN
1484241665
Erroneous ISBN
9781484241660

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Practical computer vision applications using deep learning with CNNs :
General Material Designation
[Book]
Other Title Information
with detailed examples in Python using TensorFlow and Kivy /
First Statement of Responsibility
Ahmed Fawzy Gad.

.PUBLICATION, DISTRIBUTION, ETC

Place of Publication, Distribution, etc.
[New York, NY] :
Name of Publisher, Distributor, etc.
Apress,
Date of Publication, Distribution, etc.
[2018]

PHYSICAL DESCRIPTION

Specific Material Designation and Extent of Item
1 online resource

CONTENTS NOTE

Text of Note
Introduction -- Recognition in computer vision -- Artificial neural networks -- Recognition using ANN with engineered features -- ANN optimization -- Convolutional neural networks -- TensorFlow recognition application -- Deploying pretrained models -- Cross-platform data science applications -- Appendix A: Installing your own projects using pip installer.
0

SUMMARY OR ABSTRACT

Text of Note
Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms. For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than fully connected networks. You will implement a CNN in Python to give you a full understanding of the model. After consolidating the basics, you will use TensorFlow to build a practical image-recognition application and make the pre-trained models accessible over the Internet using Flask. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads. This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production. You will: Understand how ANNs and CNNs work Create computer vision applications and CNNs from scratch using Python Follow a deep learning project from conception to production using TensorFlow Use NumPy with Kivy to build cross-platform data science applications.

ACQUISITION INFORMATION NOTE

Source for Acquisition/Subscription Address
Springer Nature
Stock Number
com.springer.onix.9781484241677

OTHER EDITION IN ANOTHER MEDIUM

International Standard Book Number
9781484241660
International Standard Book Number
9781484241684

TOPICAL NAME USED AS SUBJECT

Computer programming.
Computer vision.
Python (Computer program language)
Computer programming.
Computer vision.
Python (Computer program language)

(SUBJECT CATEGORY (Provisional

COM004000
UYQ
UYQ

DEWEY DECIMAL CLASSIFICATION

Number
006
.
3
Edition
23

LIBRARY OF CONGRESS CLASSIFICATION

Class number
Q337
.
5
Book number
.
G33
2018

PERSONAL NAME - PRIMARY RESPONSIBILITY

Gad, Ahmed Fawzy

ORIGINATING SOURCE

Date of Transaction
20200823032604.0
Cataloguing Rules (Descriptive Conventions))
pn

ELECTRONIC LOCATION AND ACCESS

Electronic name
 مطالعه متن کتاب 

[Book]

Y

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