#好Bookrecommend##好Books Curious Encounter Season# "TensorFlow Face Recognition Actual Combat", JD.com has it on sale on the same day. Supporting source code, PPT courseware, data set, development environment and Q&A service.
Face recognition technology is a biometric recognition technology based on human facial feature information. It uses a variety of measurement methods and means to scan human faces, including thermal imaging, 3D face maps, unique Landmarks) classification, etc. analyze the geometric proportions of facial features, the mapping distance between key facial features, and the texture of the skin surface.
For a long time, due to the backwardness of technical means and the complexity of human faces, face technology has not been applied on a large scale. The reason is that the face recognition technology at that time has a very low degree of recognition of the variability of people's head position, facial expression and age, it is difficult to accurately judge the target, and it cannot give a conclusion with high accuracy, thus restricting development of this technology.
With the rise of deep learning, people have found that using deep learning technology can better perform face recognition. The main advantage of deep learning methods is that they can be trained with very large datasets and learn the best features to characterize these data, so as to achieve the goal of face recognition with the required accuracy.
Contents of this book
Written on the basis of the new TensorFlow 2 version, this book teaches readers how to implement face recognition using a deep learning framework. From the basic grammar of TensorFlow 2 to the introduction of how to use TensorFlow 2 for deep learning program design, and how to design a face recognition model in practice.
This book is divided into 10 chapters. Chapters 1 and 2 introduce the basic knowledge and development path of face recognition; Chapter 3 starts with setting up the environment and introduces the installation of Anaconda, Python, PyCharm, TensorFlow CPU version and GPU version in detail; Chapter 4 Chapter 6 introduces the use of TensorFlow basic and advanced APIs; Chapter 7 introduces the method of using native API to process data and visualize the training process; Chapter 8 is practical preparation, introducing the implementation and application of the ResNet model; Chapters 9 and 10 comprehensively The knowledge in front of the book, learn the two practical projects of face recognition model and face detection.
book readers
This book is an informative tutorial for beginners and intermediate readers of artificial intelligence. Through the study of this book, readers can master the core content of deep learning and the knowledge points of face recognition under the TensorFlow framework, as well as master the whole set of skills from model building to application writing.
author of this book
Wang Xiaohua, lecturer in computer science, research direction is cloud computing, big data and artificial intelligence. Author of "Spark MLlib Machine Learning Practice", "TensorFlow Deep Learning Application Practice", "OpenCV+TensorFlow Deep Learning and Computer Vision Practice", "TensorFlow Knowledge Map Practice", "TensorFlow Face Recognition Practice", "TensorFlow Speech Recognition Practice", "TensorFlow 2.0 Volume" Accumulating Neural Networks Actual Combat", "Keras Actual Combat: Deep Learning Practice Based on TensorFlow2.2", "TensorFlow Deep Learning from Scratch", "Mathematical Principles and Implementation of Deep Learning" and other books.
