MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03630nam a22004575i 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
21661673 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20200901114416.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
|---|
| fixed length control field |
m |o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
cr ||||||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
181205s2018 xxu|||| o |||| 0|eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2019738255 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781484246757 |
| 024 7# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.1007/978-1-4842-4167-7 |
| Source of number or code |
doi |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(DE-He213)978-1-4842-4167-7 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Description conventions |
pn |
| -- |
rda |
| Transcribing agency |
DLC |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
UYQ |
| Source |
bicssc |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
COM004000 |
| Source |
bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
UYQ |
| Source |
thema |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.3 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Gad, Ahmed Fawzy. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Practical Computer Vision Applications Using Deep Learning with CNNs : |
| Remainder of title |
With Detailed Examples in Python Using TensorFlow and Kivy / |
| Statement of responsibility, etc. |
by Ahmed Fawzy Gad. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Berkeley, CA : |
| Name of producer, publisher, distributor, manufacturer |
Apress : |
| -- |
Imprint: Apress, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2018. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 online resource (XXII, 405 pages 200 illustrations) |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
computer |
| Media type code |
c |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
online resource |
| Carrier type code |
cr |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
1. Recognition in Computer Vision -- 2. Artificial Neural Network -- 3. Classification using ANN with Engineered Features -- 4. ANN Parameters Optimization -- 5. Convolutional Neural Networks -- 6. TensorFlow Recognition Application -- 7. Deploying Pre-Trained Models -- 8. Cross-Platform Data Science Applications.Appendix: Uploading Projects to PyPI. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
|---|
| Source of description note |
Description based on publisher-supplied MARC data. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Python (Computer program language). |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Open source software. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Computer programming. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Artificial Intelligence. |
| Authority record control number or standard number |
https://scigraph.springernature.com/ontologies/product-market-codes/I21000 |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Python. |
| Authority record control number or standard number |
https://scigraph.springernature.com/ontologies/product-market-codes/I29080 |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Open Source. |
| Authority record control number or standard number |
https://scigraph.springernature.com/ontologies/product-market-codes/I29090 |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
|---|
| a |
0 |
| b |
ibc |
| c |
origres |
| d |
u |
| e |
ncip |
| f |
20 |
| g |
y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Universal Decimal Classification |
| Koha item type |
Books |
