Explanation
Computer Vision Masterclass Computer Vision is a subfield of artificial intelligence that focuses on creating systems that can process, analyze and recognize visual data in a similar way to the human eye. There are many business applications in different sectors, such as: security, marketing, decision making and production. Smartphones use Computer Vision to unlock devices using facial recognition, self-driving cars use it to detect pedestrians and avoid other vehicles, and security cameras use it to detect if there are people in the area. present to sound the alarm. triggered. In this course you will learn everything you need to know to enter this world. You will learn the step-by-step implementation of 14 (fourteen) most important computer vision techniques. If you have never heard of computer vision, by the end of this course you will have a practical overview of all aspects. You will learn the basic concept about algorithms and implement a project step by step using Python language and Google Colab.
What will you learn?
- Understand the basic concepts about Cascade and HOG classifiers to detect phases
- Implement face detection using OpenCV and Dlib library
- Learn how to detect other objects using OpenCV, such as cars, clocks, eyes, and people
- Compare the results of three phase detectors: Haarcascade, HOG (Histogram of Oriented Gradients) and CNN (Convolutional Neural Networks).
- Discover faces using photos and webcam
- Understand the basic concepts about the LBPH algorithm for face recognition
- Implement face recognition using OpenCV and Dlib library
- Recognize a face using photos and a web camera
- Understand the basic concepts about KCF and CSRT algorithms to perform object tracking.
- Learn how to track objects in videos using the OpenCV library
- Learn everything you need to know about the theory behind neural networks, such as: perceptron, activation functions, weight updating, propagation, gradient classification and many more.
- Implement dense neural networks to classify images
- Learn how to extract pixels and image features to build neural networks
- Learn the theory behind adaptive neural networks and practice them using Python and TensorFlow.
Who is this course for?
- Beginners starting to learn Computer Vision
- Undergraduate students studying subjects related to Artificial Intelligence
- People who want to solve their problems using Computer Vision
- Students who want to work in companies developing Computer Vision projects
- People who want to know all the areas of Computer Vision, also know the problems that these techniques are able to solve.
- Anyone interested in Artificial Intelligence or Computer Vision
- Data scientists who want to grow their portfolios
- Professionals who want to understand how to apply Computer Vision to real projects
Computer Vision Masterclass Guide
Includes 2023-2
Requirements
- program logic
- Basic Python programming
Pictures
Sample Clip
Installation Guide
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Subtitle: English
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file size
9.47 GB