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Computer vision courses

248 Courses

California Institute of Technology XuetangX

人工智能基础

本课程全面介绍了人工智能的基础原理，涵盖四大方面：搜索与问题求解、知识与推理、学习与发现以及具体应用领域。搜索与问题求解涉及问题解决的基本原理、策略与图搜索以及博弈；知识与推理涵盖谓词逻辑、归结原理与确定性推理；学习与发现部分涉及机器学习知识，包括分类、回归和聚类算法；深度学习入门涉及图像识别、卷积神经网络、自然语言处理及循环神经网络。 通过本课程.
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California Institute of Technology XuetangX

智能车辆理论与应用

课程内容涵盖智能车辆的各个方面，如环境感知技术、深度学习及其在智能车辆上的应用、智能车辆SLAM、行为决策和运动规划等关键领域。特别介绍了智能网联技术以及智能车辆的测评体系。 本课程是国内较早开设的研究生课程，“智能车辆理论与技术”是北京理工大学机械工程学科的核心课程，通过丰富的案例理论联系实际进行讲解。
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California Institute of Technology XuetangX

现代光电图像处理方法

本课程是专业必修项目，提供32学时的校内讲授，面向学术和专业硕士研究生，每年约有60名学生参与。课程重点在于现代光电成像技术及其图像处理方法，采用专题模块的讲授形式。 每个专题全面覆盖从基本原理到理论方法，再到典型应用分析，旨在帮助学生融会贯通本科阶段所学课程，同时塑造工科思维。此外，课程紧跟技术发展前沿，重点反映当前光电图像处理方法的最新进展，拓.
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California Institute of Technology XuetangX

机器人的主动感知与行为学习

了解机器人主动感知与行为学习，探索机器人如何主动感知其环境并通过学习机制获得新行为的中文讲座。研究关键概念，如机器人感知、感觉处理和行为适应，同时了解使机器人能够智能互动和通过基于经验的学习发展日益复杂能力的基本原理。
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California Institute of Technology XuetangX

计算机是如何实现智能的

Join a captivating Chinese language lecture by Tsinghua University, delving into how computers achieve intelligence. This course uncovers the foundational concepts of artificial intelligence and computer intelligence, highlighting the processes of information processing, decision making, and the simulation of human cognitive functions. Learn.
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California Institute of Technology Udemy

TensorFlow for Deep Learning Bootcamp

TensorFlow for Deep Learning Bootcamp | Udemy Enroll in the TensorFlow for Deep Learning Bootcamp offered by Udemy and elevate your skills in Artificial Intelligence, Machine Learning, and Deep Learning. This comprehensive course is designed to provide you with hands-on experience and in-depth knowledge using TensorFlow, Google's powerful open-so.
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California Institute of Technology Udemy

Face Detection & Image Processing in Python with a FREE Book

Enhance your understanding of Computer Vision with our comprehensive course on Face Detection and Image Processing using Python and OpenCV. Whether you're a beginner or an experienced developer, this course will guide you through the essential techniques for facial recognition and image analysis. As a bonus, you'll receive a FREE Coding.
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California Institute of Technology Udemy

Computer Vision with OpenCV Python | Official OpenCV Course

Category: Python Courses Category: Computer Vision Courses Category: OpenCV Courses Category: Object Detection Courses Category: Object Tracking Courses Category: Edge Detection Courses Category: Image Manipulation Courses Category: Image Enhancement Courses Jump into the exciting world of Computer Vision with this comprehens.
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California Institute of Technology Udacity

Computer Vision

Computer Vision - Udacity Master the computer vision skills behind advances in robotics and automation. Write programs to analyze images, implement feature extraction, and recognize objects using deep learning models. University: Provider: Udacity Categories: Robotics Courses, Computer Vision Courses, Deep Learning Courses, Neural Networks Course.
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California Institute of Technology Northeastern University

Practical AI for Professionals

Practical AI for Professionals Explore key ideas in Artificial Intelligence (AI) while delving into trending developments in the field. This course examines AI tools and frameworks to enable effective and efficient collaboration across technical and non-technical stakeholders. Analyze topics such as AI-enabled perception, representation, reason.
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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!