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Mastering OpenCV with Python - Use NumPy Scikit TensorFlow and Matplotlib to learn Advanced algorithms for Machine Learning through a set of Practical Projects - cover

Mastering OpenCV with Python - Use NumPy Scikit TensorFlow and Matplotlib to learn Advanced algorithms for Machine Learning through a set of Practical Projects

Ayush Vaishya

Maison d'édition: Orange Education Pvt Ltd

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Synopsis

Unlocking Visual Insights: OpenCV Made Simple and Powerful.Book Description"Mastering OpenCV with Python" immerses you in the captivating realm of computer vision, with a structured approach that equips you with the knowledge and skills essential for success in this rapidly evolving field.From grasping the fundamental concepts of image processing and OpenCV to mastering advanced techniques such as neural networks and object detection, you will gain a comprehensive understanding. Each chapter is enriched with hands-on exercises and real-world projects, ensuring the acquisition of practical skills that can be immediately applied in your professional journey.This book not only elevates your technical proficiency but also prepares you for a rewarding career. The technological job landscape is constantly evolving, and professionals who can harness the potential of computer vision are in high demand.By mastering the skills and insights contained within these pages, you will be well-prepared to explore exciting career opportunities, ranging from machine learning engineering to computer vision research. This book is your ticket to a future filled with innovation and professional advancement within the dynamic world of computer vision.Table of Contents1. Introduction to Computer Vision2. Getting Started with Images3. Image Processing Fundamentals4. Image Operations5. Image Histograms6. Image Segmentation7. Edges and Contours8. Machine Learning with Images9. Advanced Computer Vision Algorithms10. Neural Networks11. Object Detection Using OpenCV12. Projects Using OpenCV      Index
Disponible depuis: 18/11/2023.
Longueur d'impression: 424 pages.

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