the lattice-boltzmann method

讲述Linux网络编程思想，对于学习网络编程的人来说是一个值得观看的书籍(Talking about the idea of Linux network programming is a book worth watching for those who study network programming.)

Python学习电子书经典之作，课余学习之用(Python Learning the classic books of e-books and the use of after-school learning)

统计信号处理入门，不错的通信教材(statistical signal processing entry, good communication materials)

C音频教程，基础教程，经典(C Audio Guide, Basic Course, Classic)

THE.ART.OF.Cpp—C++艺术，Herbert Schildt的全新力作，讲述了c++的新特性和对未来的展望，强烈推荐！(THE.ART.OF.Cpp-C art, Herbert Schildt's new work, described c of the new features and vision for the future and strongly recommended!)

霍夫变换和广义霍夫变换算法详细讲解和实现，英文讲义，讲解详细( Hough Transform GHT)

互相关模糊函数的matlab程序，很不错的，( cross_ambfn2.m- amodification of "ambfn1.m" for plotting cross ambiguity between two signals OF THE SAME LENGTH Designed to allow mismatch caused by FFT Doppler processing a pulse train. The two signals differ only in phases (if there is frequency mode it is the same in both signals. ambfn1.m- plots ambiguity function of a signal u_basic (row vector) The m-file returns a plot of quadrants 1 and 2 of the ambiguity function of a signal The ambiguity function is defined as: )

华为内部模拟电子教材，华为内部模拟电子教材，华为内部模拟电子教材，华为内部模拟电子教材(Huawei internal analog electronic materials, the internal analog electronic materials Huawei Huawei internal analog electronic materials, Huawei internal analog electronic materials)

关于粒子群算法的doc文档,里面包含matlab源程序(Particle swarm algorithm on the doc document, which contains the matlab source code)

说明：  本书由Keras之父、现任Google A工智能研究员的弗朗索瓦?肖莱（Frangois Chollet）执笔，详尽介 绍了用Python和Keras进行深度学习的探索实践，涉及计算机视觉、自然语言处理、生成式模型等应用。 书中包含30多个代码示例，步骤讲解详细透彻。由于本书立足于人工智能的可达性和大众化，读者无须 具备机器学习相关背景知识即可展开阅读。在学习完本书后，读者将具备搭建自己的深度学习环境、建立 图像识别模型、生成图像和文字等能力(This book is written by Frangois Chollet, the father of keras and the current researcher of Google a intelligence. It introduces in detail the exploration and practice of deep learning with Python and keras, involving computer vision, natural language processing, generative model and other applications. The book contains more than 30 code examples, and the steps are detailed and thorough. Because this book is based on the accessibility and popularization of artificial intelligence, readers can read it without having the background knowledge of machine learning. After learning this book, readers will have the ability to build their own deep learning environment, establish image recognition model, and generate images and characters)