Fundamental-of-Power-Electronics

说明：  PFLD的python实现，用于人脸的关键点检测(Python implementation of PFLD for key point detection of human face)

本手册提供规划安装、安装、调试、使用和维修传动所需的信息(This manual provides information needed for planning installation, installation, commissioning, use and maintenance of the transmission)

这本书延伸科学畅销书“全球定位系统 - 理论与实践”，以涵盖全球导航卫星系统（GNSS），包括俄罗斯GLONASS，欧洲伽利略系统，以及其他系统。书是指在一般意义到GNSS来描述各种现有参考系统的坐标和时间，卫星轨道，卫星信号，可观察，数学模型的定位，数据处理，和数据转换。本书是一本大学级入门教材，目的是作为学生的参考以及为专业人士和科学家在大地测量领域，测量工程，导航和相关学科。(This book extends the scientific bestseller GPS- Theory and Practice to cover Global Navigation Satellite Systems (GNSS) and includes the Russian GLONASS, the European system Galileo, and additional systems. The book refers to GNSS in the generic sense to describe the various existing reference systems for coordinates and time, the satellite orbits, the satellite signals, observables, mathematical models for positioning, data processing, and data transformation. This book is a university-level introductory textbook and is intended to serve as a reference for students as well as for professionals and scientists in the fields of geodesy, surveying engineering, navigation, and related disciplines.)

CPLD_FPGA的入门资料.pdf格式,非常清晰.不可多得的入门级资料.(CPLD_FPGA introductory information. Pdf format, is very clear. A rare entry-level information.)

GBT_17626.4-2008_电磁兼容_试验和测量技术_电快速瞬变脉冲群抗扰度试验.pdf(GBT_17626.4-2008_ _ EMC test and measurement techniques _ electrical fast transient burst immunity test)

基于Verilog的FPGA设计时序分析约束详细解释与使用方法(FPGA timing constraints)

Abaqus子程序Vumat初学者好用的小例子，PDF文件，内有代码和例子详解(Abaqus small example subroutine Vumat easy to use for beginners, PDF files, there are examples of code and Explanation)

华为演讲培训教程，是个不错的培训教材。欢迎大家学习。(Huawei speech training course, be a good training materials. Welcomed the U.S. study.)

一本关于黑客攻防技术的书籍，非常实用，主要是win环境下的技术，建议大家学习。(A hacker attack and defense techniques on the books, very practical, mostly win environment technology, suggest that you learn.)

说明：  A commented bash script to generate our final 2nd place solution can be found in make_kaggle_solution.sh. Running all the commands sequentially will probably take 7 - 10 days on recent consumer grade hardware. If you have multiple GPUs you can speed things up by doing training and feature extraction for the two networks in parallel. However, due to the computationally heavy data augmentation it may be far less than twice as fast especially when working with 512x512 pixel input images. You can also obtain a quadratic weighted kappa score of 0.839 on the private leaderboard by just training the 4x4 kernel networks and by performing only 20 feature extraction iterations with the weights that gave you the best MSE validation scores during training. The entire ensemble only achieves a slightly higher score of 0.845.