Description
Applied Computer Vision: Object Detection and Recognition course. This course takes you on a comprehensive journey into the world of computer vision and object recognition, from fundamental concepts to implementing and evaluating advanced models. With a hands-on approach, you will be introduced to key computer vision tasks such as image classification, object detection, semantic segmentation, and instance segmentation. This course uses popular datasets such as COCO-2017 and CamVid and frameworks such as PyTorch and FiftyOne to enhance your practical skills.
What you will learn in the Applied Computer Vision: Object Detection and Recognition course
- Understanding the basic principles of image recognition, including image classification, object recognition, and image segmentation (semantic, exemplary, and panoramic).
- Mastery of the theories and principles underlying the key models of computer vision, the ability to deeply understand their performance and applications
- Mastering the basics of PyTorch, learning how to build a CNN model and a custom image dataset
- Implementation of advanced image recognition models and their training in PyTorch
This course is suitable for people who
- Computer Science Students and Enthusiasts: Undergraduate or graduate students studying computer science, data science, artificial intelligence, or related fields who want to gain hands-on skills in image recognition using PyTorch.
- Prospective Data Scientists and Machine Learning Engineers: Individuals looking to enter the field of data science or machine learning with a particular interest in image processing and recognition techniques.
- AI and Machine Learning Enthusiasts: People who are passionate about AI and Machine Learning and want to deepen their understanding of image recognition.
- Technology Entrepreneurs: Entrepreneurs or innovators seeking to understand image recognition to implement or improve products, especially in technology-driven markets.
Specifications of Applied Computer Vision: Object Detection and Recognition course
- Publisher: Udemy
- mdrs: Vahid Mirjalili, PhD
- Training level: beginner to advanced
- Training duration: 2 hours and 14 minutes
- Number of courses: 44
Course headings
Prerequisites of Applied Computer Vision: Object Detection and Recognition course
- Python Programming Experience: Familiarity with programming, particularly in Python, as it’s the primary language used with PyTorch. Students should be comfortable with basic programming concepts and structures.
- Understanding of Basic Machine Learning Concepts: A foundational knowledge of machine learning principles, including what models are, how they are trained, and a basic understanding of concepts like classification, regression, overfitting, and underfitting.
- Introductory Knowledge of Deep Learning: Familiarity with the basic concepts of neural networks, including what they are and how they are generally structured and trained.
Course images
Sample video of the course
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download link
File(s) password: www.downloadly.ir
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860 MB
