Deep Learning in Practice III: Face Recognition

Partner: Udemy
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Description: About the courseWelcome to the course Deep Learning in Practice III on Face Recognition. I am Anis Koubaa, and I will be your instructor in this course.This course is the third course in the series Deep Learning in Practice. It provides a fast and easy-to-follow introduction to face recognition with deep learning using MTCNN for face extraction and FaceNet for face recognition. My two previous courses deal with object classification and transfer learning with Tensorflow and Keras.In this course, you will learn the whole loop of face recognition systems, which starts by extracting the face from an image and localizing the face in an image by its bounding box; then, we process the extracted face through a convolutional neural network, called FaceNet in our case, to create a fingerprint of the face, which we call face embedding. The face embedding can be stored in a database so that they are compared with other face embeddings to identify the person of interest.In this course, you will have a step-by-step introduction to this whole loop, and I will show you how you can develop a Python application that performs the abovementioned operations. Exciting, right?Why is the course important?This course is essential due to the importance of face recognition systems in real-world applications. These fast-growing systems are used in several applications, such as surveillance systems, face access systems, and biometric identification.In this course, you will be introduced to face recognition systems both from a theoretical and practical perspective, allowing you to develop your own projects using face recognition in Python.The course's motivation is a lack of resources to get quickly started with the topic. So taking this course will save you tons of time looking for scattered references over the Internet and will get you much quicker into the field.What's worth?This course provides
Category: Development > Data Science > Face Detection
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Price: 199.99
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Source: Impact
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