Computer vision courses

248 Courses

edX

Vision artificielle et exploitation intelligente des ressources naturelles

Vision artificielle et exploitation intelligente des ressources naturelles Bienvenue au cours VIARENA, «Vision artificielle et exploitation intelligente des ressources naturelles». La vision artificielle est l'art et la science de rendre les ordinateurs capables d'interpréter intelligemment des images. Il existe une multitude d'applications de la.

University of Colorado Boulder

컴퓨터 비전 분야에서의 딥 러닝 응용 사례

컴퓨터 비전 분야에서의 딥 러닝 응용 사례 이 강의는 CU 볼더 대학교의 데이터 과학 석사(MS-DS) 학위 과정의 일부로써 학점 인정이 가능하며 Coursera 플랫폼을 통해 제공됩니다. MS-DS는 CU 볼더 대학교의 응용 수학, 컴퓨터 과학, 정보 과학 및 기타 여러 학과 교수진이 모여 만든 학제간 학위 과정입니다. MS-DS는 능력에 따라 입학이 허가되고 지원 절차가 없기 때문에.

University of Colorado Boulder

Introduction to Computer Vision

Introduction to Computer Vision Introduction to Computer Vision guides learners through the essential algorithms and methods to help computers 'see' and interpret visual data. You will first learn the core concepts and techniques that have been traditionally used to analyze images. Then, you will delve into modern deep learning methods, such as.

Coursera

MathWorks Computer Vision Engineer

MathWorks Computer Vision Engineer Prepare for a career in the rapidly expanding field of computer vision. The ability to extract meaningful information from visual data is crucial for efficiently developing smart monitoring systems, enhancing medical diagnostics, and powering the next generation of autonomous vehicles. This program is designed.

Pluralsight

Getting Started with OpenCV in .NET

Getting Started with OpenCV in .NET In this course, you will become familiar with the OpenCV computer vision open source library. We will demonstrate proper usage under .NET and will write samples of common image processing tasks. The course will focus on getting you started with OpenCV and will give you the tools for further self-exploration of.

Google Cloud Skills Boost

Computer Vision Fundamentals with Google Cloud

Computer Vision Fundamentals with Google Cloud This course explores diverse computer vision use cases, highlighting machine learning strategies to address them. Strategies range from leveraging pre-built ML models through APIs and AutoML Vision to creating custom image classifiers using linear models, deep neural network (DNN) models, or con.

Introduction to Image Processing, Interactive Online Video Course: Wolfram U

Learn the fundamentals of digital image processing, including image representation and classical operations on images. This course emphasizes practical applications and understandable explanations of how image operations work. Numerous examples are included to illustrate standard applications. Make cutting-edge image processing simple with Wolf.

Udemy

TensorFlow for Deep Learning Bootcamp

TensorFlow for Deep Learning Bootcamp Learn TensorFlow by Google and become an expert in AI, Machine Learning, and Deep Learning! Provider: Udemy Categories: Machine Learning Courses, Computer Vision Courses, Deep Learning Courses, TensorFlow Courses

Pluralsight

Image Representation and Processing

Image Representation and Processing In the digital age, the ability to manipulate and understand images is not just a skill but a necessity, especially in fields like computer vision and generative AI. This course is designed to demystify the complexities of digital images, teaching you the foundational skills needed to prepare and utilize image.

Pluralsight

Azure AI Engineer Associate (AI-102): Computer Vision Solutions

Course Title: Azure AI Engineer Associate (AI-102): Computer Vision Solutions Description: Learn how to leverage the vision capabilities of Azure AI to bring insight and additional interactions with images and video. In this course, "Azure AI Engineer Associate (AI-102): Computer Vision Solutions," you’ll learn to utilize all the vision capabil.

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What is computer vision and what does its developer do?

A computer vision engineer is a specialist who teaches computers to extract information from images. In particular, automatically recognize objects or gestures in images and videos. If a person can visually determine something (for example, find a defect in a product), a computer can also be trained to do this - and thus save time and resources, simplifying many processes.

Developments in the field of computer vision courses are used in a wide variety of companies whose products are related to images or video. This includes the production of self-driving cars, helping doctors interpret MRI images when searching for tumors, and even facial recognition in the subway to identify violators of the self-isolation regime. Computer vision specialists help many e-commerce businesses reduce the burden of moderation: for example, when an ad service like Avito fights trolls who upload pictures with inappropriate content.

Computer vision specialists after computer vision courses are called differently: developers, engineers, and researchers (computer vision scientist). Essentially, a computer vision specialist is more of an engineer who uses mathematics and programming as working tools. So, globally, a computer vision engineer, a computer vision scientist, a computer vision developer and a technical vision developer are one and the same thing.

What does a computer vision developer actually do?

As a rule, the day of such a specialist begins with a stand-up with the team. He then writes code to train neural networks, preprocesses data, and analyzes experiments. A computer vision developer can work alone or in a team, where everyone performs part of a larger task.

As for working tools, the Python language is usually used to write code for experiments, and the Tensorflow or Pytorch frameworks are used to train neural networks. The work also involves special libraries for image processing such as OpenCV. For high-load projects, the C++ language can also be used, since anything written in it is executed many times faster.

Computer vision is a young, dynamically developing field at the intersection of science and engineering, in which there are still more experiments than ready-made solutions. To grow, a specialist here needs to constantly learn. But it is the novelty and non-standard nature of the tasks, as well as the opportunity to create something truly innovative, that brings many people into this profession.

What do they teach in computer vision classes at AI Education?

Training at the best computer vision course typically consists of three modules: creating infrastructure, basics of machine learning and studying computer vision.

The first block at a computer vision online course can be called introductory. Since specialists in the field of computer vision rely on knowledge of mathematics and programming when solving problems, at the start they will have to study from scratch or brush up on topics from higher mathematics, mathematical analysis and linear algebra, as well as work with the Python language. Don’t worry if your knowledge is limited to school mathematics, which was “long ago and not true”: we will help you improve the necessary topics in the first module, so that in the future all students can move through the program at the same rhythm.

The second module is entirely devoted to machine learning. It helps solve computer vision problems faster and easier. For example, for facial recognition, you can expertly describe facial features based on questions that are asked when compiling an identikit. Or you can feed the algorithm a lot of photographic portraits with markings about which face belongs to whom, and then the algorithm itself will learn to extract features by which faces can be identified. In the future, if you need to determine who is in the photo, the algorithm will only need a database of portraits. If there is a photo of the person you need, the system itself will easily find him.

In the second module you will examine probability theory and mathematical statistics. Students will practice solving problems using fundamental algorithms and data structures in Python, become familiar with Python libraries for Data Science (NumPy, Matplotlib), as well as machine learning algorithms.

Finally, in the third module at this machine vision course you will analyze the main tasks of computer vision, we will work with mathematical morphology and the OpenCV and PIL libraries!