粒子群算法源程序

说明：  本书主要针对对人工智能感兴趣，并想要零基础学习强化学习的从事机器学习开发人 员和热衷于深度学习的爱好者。(Reinforcement learning)

C 语言入门最最经典的一本，作者是 K&R，其中的 R （Dennis M.Ritchie）为 C 语言设计者之一。(The most classic introduction to C language is by K&R, among which R (Dennis M. Ritchie) is one of the C language designers.)

MFC简明教程，来自实践(MFC from practice)

说明：  模式识别工具箱函数，包含多种模式识别方法如KNN，PCA，SVM,C4.5,EM算法等等(Pattern recognition toolbox functions, including a variety of pattern recognition methods such as KNN, PCA, SVM, C4.5, EM algorithm, etc.)

说明：  基于核主成分分析法的GA_BP神经网络短期用电负荷预测，很不错的文章研究遗传算法，BP神经网络，核主成分(GA_BP Neural Network Based on Kernel Principal Component Analysis (KPCA) for short-term load forecasting. This paper studies genetic algorithm, BP Neural Network and Kernel Principal Component.)

说明：  《深度学习》由全球知名的三位专家Ian Goodfellow、Yoshua Bengio 和Aaron Courville撰写，是深度学习领域奠基性的经典教材。全书的内容包括3个部分：第1部分介绍基本的数学工具和机器学习的概念，它们是深度学习的预备知识；第2部分系统深入地讲解现今已成熟的深度学习方法和技术；第3部分讨论某些具有前瞻性的方向和想法，它们被公认为是深度学习未来的研究重点。(Deep learning, written by three famous experts in the world, is a classic textbook in the field of deep learning. The book consists of three parts: the first part introduces the basic mathematical tools and the concept of machine learning, which are the preparatory knowledge of deep learning; the second part systematically explains the mature deep learning methods and technologies; the third part discusses some forward-looking directions and ideas, which are recognized as the future research focus of deep learning.)

说明：  Verilog HDL基本语法，是本人遇到的比较简洁明了的语法学习笔记(Verilog HDL basic grammar , a relatively simple and clear grammar learning note)

饱受赞誉的Python入门教程，语言十分平易近人，本资源是非扫描PDF版(The highly acclaimed Python introductory tutorial is very accessible in language. This resource is a non-scanned PDF version.)

说明：  华为硬件工程师手册，重要学习资料，大家一起学习，欢迎下载。(Huawei hardware engineer manual, it is important to learning materials, learning together, please download.)

ofdm system simulation including 16qam modulation fft windowing modules plus cp, Implemented with SDRAM run nios, while saving camera data SRAM, Stepwise linear regression.