Computer vision courses

248 Courses

XuetangX

AI TIME PhD 华南理工实验室专场二

Join an exclusive academic session that delves into pioneering research and discussions from the AI TIME PhD program at South China University of Technology laboratory. This event focuses on the latest advancements in artificial intelligence and technological innovations.

人工智能基础核心技术之事件检测

欢迎参加由清华大学提供的“人工智能基础核心技术之事件检测”课程。在此课程中，您将学习如何运用人工智能中的事件检测技术来识别和分析数据流中的重大事件或模式。这是一门中文课程，旨在帮助您深入理解事件检测在人工智能中的应用。本课程涵盖多个关键主题，包括人工智能、机器学习、计算机视觉、神经网络、时间序列分析、信号处理及模式识别。这为您在这些领域的进一.

Coursera

Introducing Multimodal Llama 3.2

Join the short course 'Introducing Multimodal Llama 3.2' and dive into the latest innovations in AI brought to you by Amit Sangani, Senior Director of AI Partner Engineering at Meta. Explore the enhancements in the Llama models 3.1 and 3.2, including custom tool calling, multimodality, and the Llama stack. Llama models, spanning from 1B to 40.

Coursera

IBM AI Engineering

Coursera AI technology is anticipated to expand by 37.3% by the year 2030, according to Forbes. The IBM AI Engineering Professional Certificate, offered through Coursera, equips data scientists, machine learning engineers, software engineers, and technical specialists with skills to excel as AI engineers. Throughout the program, participa.

SAP Learning

Building AI and Sustainability Solutions on SAP BTP

Discover how SAP addresses sustainability goals by enabling customers to evolve into intelligent, sustainable enterprises. The SAP Business Technology Platform (BTP) serves as a robust foundation for developing applications, integrating solutions, managing data, deploying analytics, and leveraging AI. SAP's commitment to sustainability is exempl.

Microsoft Learn

Ethical AI

Ethical AI Enhance your understanding of ethical AI with Microsoft's comprehensive course. This program covers critical areas such as AI workloads and Azure AI Services, emphasizing Microsoft's commitment to Responsible AI policies. Module 1: Explore AI solutions and the essentials of responsible AI practices. Discover the potential.

SAP Learning

Generative AI at SAP

Discover how artificial intelligence (AI) transforms business processes with the 'Generative AI at SAP' course. This program provides a comprehensive understanding of AI's fundamental uses and benefits in a professional setting. Participants will explore different AI methodologies and study detailed use cases. Upon completion, attendees will gain.

LinkedIn Learning

Develop Your Skills with the OpenAI API

Develop Your Skills with the OpenAI API The popularity of OpenAI's powerful machine learning models enables developers to create amazing AI-powered applications. The OpenAI API serves as the bridge to these powerful models. Through this learning path, developers can explore the OpenAI API's capabilities and start building powerful AI applications.

AWS Skill Builder

AWS Cloud Quest: Machine Learning (Japanese)

Explore the dynamic world of cloud computing and machine learning with AWS Cloud Quest: Machine Learning - offered in Japanese by AWS Skill Builder. This course is your gateway to mastering essential concepts such as: Cloud computing fundamentals with Amazon S3. Introductory cloud steps using Amazon EC2 and AWS infrastructure. Estimat.

Coursera

Üretken Yapay Zekaya Giriş

Üretken yapay zekaya giriş yaparak bu heyecan verici ve hızla gelişen alan hakkında temel bilgileri edinin. Kurs, üretken yapay zekanın ne olduğunu, nasıl işlediğini ve geleneksel makine öğrenim yöntemlerinden nasıl farklı olduğunu açıklamayı hedefliyor. Öğrenme sürecinizde size rehberlik etmek için Google'ın güçlü araçlarını tanıyacak ve uy.

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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!