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Computer vision courses

248 Courses

California Institute of Technology XuetangX

微软--计算机视觉论文分享课

随着云计算、大数据和机器学习的迅速发展，计算机视觉作为人工智能领域的核心技术之一，近年来取得了诸多突破性进展。如今，不论是在学术界、工业界，还是在初创企业中，计算机视觉都备受关注！CVPR会议是由IEEE主办的计算机视觉和模式识别领域的顶级会议。为了推动国内计算机视觉研究的发展，强化工业界与学术界的交流，微软亚洲研究院主办，清华大学媒体与网络技术教育部-微.
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California Institute of Technology XuetangX

计算机视觉技术及应用

本课程提供图像采集、数据文件整理、图像清洗、图像增广、可视化图像检测结果、图像标注、视频标注、标注文件格式转换等全面的学习体验。您将获得对视觉应用场景的深入认知与实际部署的能力。
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California Institute of Technology XuetangX

计算机视觉

本课程旨在为学生介绍机器视觉技术的基本任务及发展现状，帮助学生理解和掌握机器视觉的基础理论和应用方法，为进一步研究打下必要基础。 提供者: XuetangX 分类: 计算机视觉课程, 图像识别课程, 3D重建课程, 边缘检测课程, 图像去噪课程
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California Institute of Technology XuetangX

人工智能技术与应用

This course is tailored for engineering management graduate students, integrating artificial intelligence theory, experiments, and engineering practice. Theoretical topics include an introduction to AI, knowledge representation and graphs, search strategies, genetic algorithms, swarm intelligence, neural networks, machine learning, deep learni.
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California Institute of Technology XuetangX

自动驾驶控制算法基础训练营

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California Institute of Technology XuetangX

Introduction to Artificial Intelligence

This course offers a comprehensive introduction to the fundamental concepts, history, and algorithms of Artificial Intelligence. Participants will gain insights into key AI terminologies such as data processing, machine learning, deep learning, and neural networks. Additionally, the course covers practical AI applications in areas like classifi.
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California Institute of Technology Udemy

Convolutional Neural Networks in Python: CNN Computer Vision

Enhance your skills in computer vision and image recognition with our specialized course on Convolutional Neural Networks (CNNs) in Python. Dive into deep learning techniques and gain proficiency in state-of-the-art frameworks like Keras and TensorFlow 2. Join us on Udemy to take your understanding of machine learning to the next level. Offer.
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California Institute of Technology Udemy

Master Computer Vision™ OpenCV4 in Python with Deep Learning

Develop your computer vision expertise with the "Master Computer Vision™ OpenCV4 in Python with Deep Learning" course. Dive into the world of OpenCV4 and enhance your skills in Dlib and deep learning using Keras, TensorFlow, and Caffe. This comprehensive program offers you 21 real-life projects that bolster your understanding and application of.
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California Institute of Technology XuetangX

人工智能与生物特征识别

课程名称：人工智能与生物特征识别 课程描述：本课程为专业选修课程，北京理工大学研究生精品课程，学时为32学时，向研究生及高年级本科生开放。课程通过理论与实践的结合，强化学生在相关领域的能力。基于本课程，历届学生成绩显著，多次在中国研究生电子竞赛中斩获全国及区域多项大奖。课程优势在于全面介绍智能成像与信息感知技术，结合机器学习和深度学习技术，指导学.
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California Institute of Technology XuetangX

Introduction to Artificial Intelligence

Gain a comprehensive understanding of artificial intelligence with our extensive course, incorporating knowledge from diverse fields such as philosophy, brain science, mathematics, and computing. This program delves into various AI types, including perceptual, cognitive, language, and behavioral intelligence. Discover fundamental concepts and st.
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“Computer vision student” sounds like a quote from science fiction, don’t you think? In fact, a computer vision engineer is a profession that, although it has not yet become the most widespread, is already rapidly gaining popularity and offers high salaries even at the start of a career.

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!