Computer vision courses

248 Courses

Udemy

Deep Learning: Convolutional Neural Networks in Python

Embark on a comprehensive journey into the realm of Deep Learning with our course on Convolutional Neural Networks (CNNs) using Python. Dive into TensorFlow 2 and empower your skills in fields such as Computer Vision and Natural Language Processing (NLP). Ideal for those interested in Data Science and Machine Learning, this course will enhance y.

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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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人工智能

课程结构分为三部分，共八章内容，涵盖人工智能导引、大数据、机器学习、计算机视觉、智能语音、自然语言处理、智能机器人以及教育应用与伦理安全。课程介绍了人工智能的核心技术，与教育应用紧密结合，并设计实践环节，采用理论与案例结合的启发式教学方法，激发同学们对人工智能的学习热情。课程特点包括：理论讲授：通过课件、多媒体等方式讲解人工智能技术。理.

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5G与人工智能

《5G与人工智能》是一门专为社会学习者和本科生开设的通识课程，面向希望了解未来工作和新技术发展的学员。无论是对5G、人工智能或相关领域工作有意向的文、理、工科学生，均可选修此课程。课程目标在于让学员掌握当下热门的科技话题，包括5G和人工智能的基础知识，并为未来就业做好准备。课程响应国家战略，通过全新视角，以浅显易懂的方式进行阐述，并引导解决部分难点。.

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人工智能教育应用

人工智能被视为继蒸汽机、电力、互联网之后最有可能带来新的产业革命浪潮的技术，而教育领域则是人工智能技术影响最为深刻的领域之一。无论你是在校学生还是一线教师，只要对人工智能和教育感兴趣，都可以来学习这门课程。这门课程将介绍人工智能的核心技术与教育创新应用场景，帮助你深刻认识人工智能对教育体系的变革与推动作用，以及未来人工智能在教育领域的发展趋势.

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AI TIME CVPR 专场一

Join the AI TIME CVPR 专场一 to delve into the forefront of AI research and developments in the realm of computer vision. This session, conducted in Chinese, is part of the CVPR conference and offers attendees an invaluable opportunity to enhance their understanding of the latest innovations and practical applications in artificial intellige.

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AI TIME PhD 浙江大学CAD&CG国家重点实验室专场一

Visit provider Immerse yourself in the latest advancements in artificial intelligence during AI TIME PhD, where Zhejiang University's State Key Laboratory of CAD&CG hosts a specialized session in Chinese. This engaging lecture covers innovative research and future trends in AI, including computer-aided design and computer graphics. This.

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AI TIME CVPR 专场四

Delve into a specialized conference session dedicated to Artificial Intelligence and Computer Vision, hosted in Chinese and featuring presentations from leading experts at the Conference on Computer Vision and Pattern Recognition (CVPR). Discover the latest developments and innovative methodologies emerging in the fields of AI and computer vis.

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AI TIME PhD 华南理工实验室专场一

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AI TIME CVPR 专场三

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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!