國立虎尾科技大學 |

Practical Machine Learning and Image Processing = For Facial Recognition, Object Detection, and Pattern Recognition Using Python /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Practical Machine Learning and Image Processing/ by Himanshu Singh.
其他題名:	For Facial Recognition, Object Detection, and Pattern Recognition Using Python /
作者:	Singh, Himanshu.
面頁冊數:	XV, 169 p. 91 illus., 14 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-1-4842-4149-3
ISBN:	9781484241493

Practical Machine Learning and Image Processing = For Facial Recognition, Object Detection, and Pattern Recognition Using Python /
Singh, Himanshu.

Practical Machine Learning and Image ProcessingFor Facial Recognition, Object Detection, and Pattern Recognition Using Python /[electronic resource] :by Himanshu Singh. - 1st ed. 2019. - XV, 169 p. 91 illus., 14 illus. in color.online resource.

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. You will: Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects.

ISBN: 9781484241493

Standard No.: 10.1007/978-1-4842-4149-3doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Practical Machine Learning and Image Processing = For Facial Recognition, Object Detection, and Pattern Recognition Using Python /
LDR:02848nam a22003735i 4500 001 1009795
003 DE-He213
005 20200701122343.0
007 cr nn 008mamaa
008 210106s2019 xxu| s |||| 0|eng d
020 $a 9781484241493 $9 978-1-4842-4149-3
024 7 $a 10.1007/978-1-4842-4149-3 $2 doi
035 $a 978-1-4842-4149-3
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Singh, Himanshu. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1303798
245 1 0 $a Practical Machine Learning and Image Processing $h [electronic resource] : $b For Facial Recognition, Object Detection, and Pattern Recognition Using Python / $c by Himanshu Singh.
250 $a 1st ed. 2019.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2019.
300 $a XV, 169 p. 91 illus., 14 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
520 $a Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. You will: Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects.
650 0 $a Artificial intelligence. $3 559380
650 0 $a Programming languages (Electronic computers). $3 1127615
650 0 $a Open source software. $3 561177
650 0 $a Computer programming. $3 527822
650 0 $a Python (Computer program language). $3 1127623
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Programming Languages, Compilers, Interpreters. $3 669782
650 2 4 $a Open Source. $3 1113081
650 2 4 $a Python. $3 1115944
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484241486
776 0 8 $i Printed edition: $z 9781484241509
856 4 0 $u https://doi.org/10.1007/978-1-4842-4149-3
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)