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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-in-practice-iii/
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课程名称：深度学习实践 III：人脸识别 课程概述： 欢迎来到深度学习实践 III 人脸识别课程。我是Anis Koubaa，将担任本课程的讲师。本课程是深度学习实践系列的第三门课程，旨在提供一个快速且易于遵循的人脸识别深度学习入门。课程将使用MTCNN进行人脸提取，并采用FaceNet进行人脸识别。前两门课程分别关注物体分类和使用Tensorflow与Keras进行迁移学习。 在本课程中，您将学习人脸识别系统的整个流程：首先从图像中提取人脸并定位其边界框；然后通过卷积神经网络（在本课程中为FaceNet）处理提取的人脸，以生成我们称之为人脸嵌入的指纹。人脸嵌入可以存储在数据库中，以便与其他人脸嵌入进行比较，从而识别目标人物。 课程的重要性： 人脸识别系统在现实世界中的应用越来越重要，广泛用于安防系统、人脸识别访问系统以及生物识别识别等多个领域。本课程将从理论和实践两个角度介绍人脸识别系统，使您能够使用Python开发自己的项目。 课程的动机在于缺乏快速入门的资源，因此本课程将节省您大量在互联网上寻找分散参考的时间，让您更快地进入这一领域。 课程内容： 本课程提供对人脸识别系统的快速且全面的覆盖，使您能够从零开始构建人脸识别应用。课程首先介绍人脸识别系统的基本概念，以及人脸嵌入的深度学习模型如何训练和生成。然后，通过使用Python编程语言的MTCCN进行人脸提取和FaceNet进行人脸识别，提供实践介绍。我将提供一个Jupyter Notebook，您可以在课堂上跟随讲解并编写代码。 在课程结束时，您将理解人脸识别系统的整个流程，并能够开发您的应用并将其集成到您的项目中。 先决条件： 为了最大限度地受益于本课程，您只需要了解Python编程基础。如果您对深度学习和TensorFlow有基本了解，将会更有帮助，但不是必要条件。无论如何，您可以参考我的前两门课程：《深度学习实践 I》和《深度学习实践 II》，以获得深度学习的基本实践介绍。 关于我： 我是一名计算机科学的全职教授，同时也是王子苏丹大学机器人与物联网实验室的负责人。我是两门畅销课程的作者，内容涉及深度学习和机器人操作系统（ROS），而本课程是深度学习实践系列中的第三门，专注于人脸识别系统。该系列课程旨在以简单易懂的方式向希望快速入门实践项目的初学者介绍深度学习的高级主题。前两门课程分别涉及物体分类和迁移学习项目。 欢迎来到本课程，祝您学习愉快！让我们开始学习吧！
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 fast yet comprehensive coverage of face recognition systems that would let you go from Zero to Hero.I first start with presenting the fundamental concepts of face recognition systems and how deep learning models for face embedding are trained and produced.Then, I provide a hands-on introduction to face recognition using MTCCN for face extraction and FaceNet for face recognition, all with Python programming language. Tensorflow and Keras APIs will be used to load the FaceNet model. I provide a Jupiter notebook that you will use as a guide in the lecture to follow and write the code to apply as you learn.At the end of this course, I guarantee that you will understand the whole loop of face recognition systems, and you will be able to develop your application and integrate it into your project.Pre-requisitesTo benefit from this course most, you just need to know about Python programming.Having a basic understanding of deep learning and TensorFlow would be a plus, but it is not mandatory.In any case, you may refer to my two courses: Deep Learning in Practice I and II, for a basic practical introduction to deep learning.Welcome to the course, and I wish you a pleasant learning experience.Let's get started.About meI am Anis Koubaa, and I am working as a Full Professor in Computer Science and Leader of the Robotics and Internet-of-Things Lab at Prince Sultan UniversityI am the author of two best-seller courses on Deep Learning and Robot Operating System (ROS),and this course is the third course in the series Deep Learning in Practice, which deals with face recognition systems.The series of deep learning in practice intends to present advanced deep learning topics very easily to beginner users who would like to get started with hands-on projects in deep learning in a minimum amount of time.The two previous courses dealt with object classification and transfer learning projects.
