ByLighTing
Scalability Tester
2
MONTHS
2 2 MONTHS OF SERVICE
LEVEL 1
400 XP
- Understand the basic intuition about Cascade and HOG classifiers to detect faces
- Implement face detection using OpenCV and Dlib library
- Learn how to detect other objects using OpenCV, such as cars, clocks, eyes, and full body of people
- Compare the results of three face detectors: Haarcascade, HOG (Histogram of Oriented Gradients) and CNN (Convolutional Neural Networks)
- Detect faces using images and the webcam
- Understand the basic intuition about LBPH algorithm to recognize faces
- Implement face recognition using OpenCV and Dlib library
- Recognize faces using images and the webcam
- Understand the basic intuition about KCF and CSRT algorithms to perform object tracking
- Learn how to track objects in videos using OpenCV library
- Learn everything you need to know about the theory behind neural networks, such as: perceptron, activation functions, weight update, backpropagation, gradient descent and a lot more
- Implement dense neural networks to classify images
- Learn how to extract pixels and features from images in order to build neural networks
- Learn the theory behind convolutional neural networks and implement them using Python and TensorFlow
- Implement transfer learning and fine tuning to get incredible results when classifying images
- Use convolutional neural networks to classify the following emotions in images and videos: happy, anger, disgust, fear, surprise and neutral
- Compress images using linear and convolutional autoencoders
- Detect objects in images in videos using YOLO, one of the most powerful algorithms today
- Recognize gestures and actions in videos using OpenCV
- Learn how to create hallucinogenic images with Deep Dream
- Learn how to revive famous artists with style transfer
- Create images that don’t exist in the real world with GANs (Generative Adversarial Networks)
- Implement image segmentation do extract useful information from images and videos
- Programming logic
- Basic Python programming
- Beginners who are starting to learn Computer Vision
- Undergraduate students who are studying subjects related to Artificial Intelligence
- People who want to solve their own problems using Computer Vision
- Students who want to work in companies developing Computer Vision projects
- People who want to know all areas inside Computer Vision, as well as 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 portfolio
- Professionals who want to understand how to apply Computer Vision to real projects
You must upgrade your account or reply in the thread to view hidden text.