Practical Computer Vision Applications Using Deep Learning with CNNs : (Record no. 225057)

MARC details
000 -LEADER
fixed length control field 02605cam a22002415i 4500
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781484246757
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Item number GAD.P
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.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2018.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. Apress
Date of publication, distribution, etc. 2018
300 ## - PHYSICAL DESCRIPTION
Extent 405 pages
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.
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.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Python.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Open Source.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Book
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
        Campus Library Kariavattom Campus Library Kariavattom Processing Center 06/08/2021   006.3 GAD.P UCL30923 06/08/2021 06/08/2021 Book