2 Computer Vision courses in Manchester

Learn to build highly sophisticated deep learning and Computer Vision applications with PyTorch

24 Hour Flash Deal **25-in-1 Computer Vision: C++ and OpenCV with GPU support Mega Bundle** Computer Vision: C++ and OpenCV with GPU support Enrolment Gifts **FREE PDF Certificate**FREE PDF Transcript ** FREE Exam** FREE Student ID ** Lifetime Access **FREE Enrolment Letter ** Take the initial steps toward a successful long-term career by studying the Computer Vision: C++ and OpenCV with GPU support package online with Studyhub through our online learning platform. The Computer Vision: C++ and OpenCV with GPU support bundle can help you improve your CV, wow potential employers, and differentiate yourself from the mass. This Computer Vision: C++ and OpenCV with GPU support course provides complete 360-degree training on Computer Vision: C++ and OpenCV with GPU support. You'll get not one, not two, not three, but twenty-five Computer Vision: C++ and OpenCV with GPU support courses included in this course. 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Here's what you get: Step by step Computer Vision: C++ and OpenCV with GPU support lessons One to one assistance from Computer Vision: C++ and OpenCV with GPU supportprofessionals if you need it Innovative exams to test your knowledge after the Computer Vision: C++ and OpenCV with GPU supportcourse 24/7 customer support should you encounter any hiccups Top-class learning portal Unlimited lifetime access to all twenty-five Computer Vision: C++ and OpenCV with GPU support courses Digital Certificate, Transcript and student ID are all included in the price PDF certificate immediately after passing Original copies of your Computer Vision: C++ and OpenCV with GPU support certificate and transcript on the next working day Easily learn the Computer Vision: C++ and OpenCV with GPU support skills and knowledge you want from the comfort of your home CPD 250 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Computer Vision: C++ and OpenCV with GPU support training is suitable for - Students Recent graduates Job Seekers Individuals who are already employed in the relevant sectors and wish to enhance their knowledge and expertise in Computer Vision: C++ and OpenCV with GPU support Requirements To participate in this Computer Vision: C++ and OpenCV with GPU support course, all you need is - A smart device A secure internet connection And a keen interest in Computer Vision: C++ and OpenCV with GPU support Career path You will be able to kickstart your Computer Vision: C++ and OpenCV with GPU support career because this course includes various courses as a bonus. This Computer Vision: C++ and OpenCV with GPU support is an excellent opportunity for you to learn multiple skills from the convenience of your own home and explore Computer Vision: C++ and OpenCV with GPU support career opportunities. 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The Computer Vision course with C++ and OpenCV with GPU support provides an introduction to computer vision and its applications using C++ and OpenCV with GPU acceleration. Students will learn about setting up the necessary environments, basic examples, background segmentation, object detection with OpenCV's ML module using C++ and CUDA, and optical flow. Learning Outcomes: Set up the required environments for C++ and OpenCV with GPU support. Understand the fundamentals of computer vision and its applications. Implement background segmentation techniques to extract relevant objects from the environment. Use OpenCV's ML module with C++ and CUDA to perform object detection efficiently. Apply optical flow algorithms to track object movements and analyze motion patterns. Why buy this Computer Vision: C++ and OpenCV with GPU support? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Computer Vision: C++ and OpenCV with GPU support there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Computer Vision: C++ and OpenCV with GPU support course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Computer Vision: C++ and OpenCV with GPU support does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Computer Vision: C++ and OpenCV with GPU support was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Computer Vision: C++ and OpenCV with GPU support is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Unit 01: Set up Necesssary Environments Module 01: Driver installation 00:06:00 Module 02: Cuda toolkit installation 00:01:00 Module 03: Compile OpenCV from source with CUDA support part-1 00:06:00 Module 04: Compile OpenCV from source with CUDA support part-2 00:05:00 Module 05: Python environment for flownet2-pytorch 00:09:00 Unit 02: Introduction with a few basic examples! Module 01: Read camera & files in a folder (C++) 00:11:00 Module 02: Edge detection (C++) 00:08:00 Module 03: Color transformations (C++) 00:07:00 Module 04: Using a trackbar (C++) 00:06:00 Module 05: Image filtering with CUDA (Introduction to using OpenCV GPU methods on C++) 00:13:00 Unit 03: Background segmentation Module 01: Background segmentation with MOG (C++) 00:04:00 Module 02: MOG and MOG2 cuda implementation (C++ - CUDA) 00:03:00 Module 03: Special app: Track class 00:06:00 Module 04: Special app: Track bgseg Foreground objects 00:08:00 Unit 04: Object detection with openCV ML module (C++ CUDA) Module 01: A simple application to prepare dataset for object detection (C++) 00:08:00 Module 02: Train model with openCV ML module (C++ and CUDA) 00:13:00 Module 03: Object detection with openCV ML module (C++ CUDA) 00:06:00 Unit 05: Optical Flow Module 01: Optical flow with Farneback (C++) 00:08:00 Module 02: Optical flow with Farneback (C++ CUDA) 00:06:00 Module 03: Optical flow with Nvidia optical flow SDK (C++ CUDA) 00:05:00 Module 04: Optical flow with Nvidia Flownet2 (Python) 00:05:00 Module 05: Performance Comparison 00:07:00 Additional Resource Resources - Computer Vision: C++ and OpenCV with GPU support 00:00:00 Assignment Assignment - Computer Vision: C++ and OpenCV with GPU support 00:00:00

Computer vision (CV), a subfield of computer science, focuses on replicating the complex functionalities of the human visual system. This course provides a comprehensive understanding of Computer Vision from the beginning using Python and helps you in becoming an expert.

Overview This comprehensive course on Computer Vision: C++ and OpenCV with GPU support will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Computer Vision: C++ and OpenCV with GPU support comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Computer Vision: C++ and OpenCV with GPU support. It is available to all students, of all academic backgrounds. Requirements Our Computer Vision: C++ and OpenCV with GPU support is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 5 sections • 22 lectures • 02:31:00 total length •Module 01: Driver installation: 00:06:00 •Module 02: Cuda toolkit installation: 00:01:00 •Module 03: Compile OpenCV from source with CUDA support part-1: 00:06:00 •Module 04: Compile OpenCV from source with CUDA support part-2: 00:05:00 •Module 05: Python environment for flownet2-pytorch: 00:09:00 •Module 01: Read camera & files in a folder (C++): 00:11:00 •Module 02: Edge detection (C++): 00:08:00 •Module 03: Color transformations (C++): 00:07:00 •Module 04: Using a trackbar (C++): 00:06:00 •Module 05: Image filtering with CUDA (Introduction to using OpenCV GPU methods on C++): 00:13:00 •Module 01: Background segmentation with MOG (C++): 00:04:00 •Module 02: MOG and MOG2 cuda implementation (C++ - CUDA): 00:03:00 •Module 03: Special app: Track class: 00:06:00 •Module 04: Special app: Track bgseg Foreground objects: 00:08:00 •Module 01: A simple application to prepare dataset for object detection (C++): 00:08:00 •Module 02: Train model with openCV ML module (C++ and CUDA): 00:13:00 •Module 03: Object detection with openCV ML module (C++ CUDA): 00:06:00 •Module 01: Optical flow with Farneback (C++): 00:08:00 •Module 02: Optical flow with Farneback (C++ CUDA): 00:06:00 •Module 03: Optical flow with Nvidia optical flow SDK (C++ CUDA): 00:05:00 •Module 04: Optical flow with Nvidia Flownet2 (Python): 00:05:00 •Module 05: Performance Comparison: 00:07:00

In this course, you will be learning one of the widely used deep learning frameworks, that is, PyTorch, and learn the basics of convolutional neural networks in PyTorch. We will also cover the basics of Python and understand how to implement different Python libraries.

This course is designed for beginners or anyone who wants to get started with Python-based face recognition. In this course, you will learn how to perform face detection from images, face detection from real-time videos, emotion detection, age-gender prediction, face recognition from images and real-time videos, and more!

This course is a quick starter for anyone looking to delve into optical character recognition, image recognition, object detection, and object recognition using Python without having to deal with all the complexities and mathematics associated with a typical deep learning process.