深度学习资料

机器学习实战中文版，基于Python语言，适合初学者(Machine Learning in Action(Python))

机器学习算法中使用决策树算法进行项目训练并测试

Adaboost体现的是“三个臭皮匠，胜过一个诸葛亮”，它是一种迭代算法，其核心思想是针对同一个训练集训练不同的分类器(弱分类器)， 然后把这些弱分类器集合起来，构成一个更强的最终分类器（强分类器）(Adaboost embodies "three smelly cobblers are better than one Zhuge Liang". It is an iterative algorithm. Its core idea is to train different classifiers (weak classifiers) for the same training set. Then these weak classifiers are combined to form a stronger final classifier (strong classifier).)

一个python写的验证码识别程序，依赖PIL、Tesseract-OCR,包括简单的噪点去除等等，识别率还不错

机器学习实战 第四章实例代码 美国 Machine Learning in　Action 4 Charpter instance(Machine Learning in　Action 4 Charpter instance)

说明：  粒子算法，轨迹规划参考，适用于无人驾驶，紧急避障等(Particle algorithm, trajectory planning reference, suitable for unmanned driving, emergency obstacle avoidance, etc)

abaqus 施加周期性边界条件脚本，通过General_Mpc_Cube输入正确的模型和部件文件运行。(ABAQUS applies periodic boundary conditions to scripts)

用到的数据是2015微博用户画像比赛提供的数据，数据包含在文件中。这份代码能够实现完整的提取特征到训练模型，从而的出预测结果的功能。用到了sklearn及panda包。(The data used is provided by the user portrait competition of microblog in 2015. This code can complete the function of extracting features to the training model, thus producing the prediction results. Sklearn and panda packages are used.)

nao机器人打高尔夫,指令盒版实现nao机器人识别小球和打击动作(nao robot play golf(behavior boxes version))

说明：  可以对照着《纺织复合材料细观力学分析的一般性周期性边界条件及其有限元实现_张超》来理解周期性边界条件(The periodic boundary conditions can be understood by referring to the general periodic boundary conditions and the Finite element Realization of Textile Composites micromechanics)