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Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python - Softcover
Inhaltsangabe
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.
What You Will Learn
- 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
Who This Book Is For
Data scientists and software developers interested in image processing and computer vision.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Über die Autorin bzw. den Autor
Himanshu Singh has more than five years of experience as a data science professional. Currently he is senior data scientist at Unify Technologies Private Limited. He gives corporate training on data science, ML, and DL. He's also a visiting faculty for analytics at the Narsee Monjee Institute of Management Studies, considered one of the premium management institutes in India. He is founder of Black Feathers Analytics and Rise of Literati Clubs.
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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
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
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Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python
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Taschenbuch. Zustand: Neu. Neuware -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. Yoüll see the OpenCV algorithms and how to use them for image processing.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 188 pp. Englisch. Artikel-Nr. 9781484241486
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