Packt – Computer Vision Theory and Projects in Python for Beginners

Packt – Computer Vision Theory and Projects in Python for Beginners-XQZT
English | Size: 16.13 GB
Category: Tutorial

Explore the world of Computer Vision and learn to master it using Python, OpenCV, TensorFlow, and others
The high-quality content of the Mastering Computer Vision from the Absolute Beginning Using Python course presents you with a great opportunity to learn and become an expert. You will learn the core concepts of the CV field. This course will also help you understand the digital imaging process and identify the key application areas of CV. The course is easy to understand, descriptive, comprehensive, practical with live coding, and rich with state-of-the-art and updated knowledge of this field.

Although this course is a compilation of all the basic concepts of CV, you are encouraged to step up and experience more than what you learn. Your understanding of every concept is tested at the end of each section. The homework assignments/tasks/activities/quizzes along with solutions will assess your learning. Several of these activities are focused on coding so that you are ready to run with implementations.

The two hands-on projects in the last section-Change Detection in CCTV Cameras (Real-Time) and Smart DVRs (Real-Time)-make up the most important learning element of this course. They will help you sharpen your practical skills. Successful completion of these two projects will help you enrich your portfolio and kick-start your career in the CV field.

By the end of the course, you will have a strong understanding of Computer Vision concepts and will be ready to apply them in your future projects.

Another exquisit release

Packt.Computer.Vision.Theory.and.Projects.in.Python.for.Beginners-XQZT

Title: Computer Vision Theory and Projects in Python for Beginners
Publisher: Packt
Category: Data
Size: 16542M
Files: 27F
Date: 2021-09-24
Course #: 9781801815949
Published: Packt
Updated: N/A
Author: AI Sciences
Duration: 26 hours 49 minutes
Exer/Code:

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