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