问卷调查（含匿名处理）

【实例简介】

了解基本编程知识，熟悉编程算法，递归算法画五角星。(Understand basic programming knowledge, familiar with programming algorithm, recursive algorithm drawing pentagon.)

说明：  利用已经使用的分数文本判断文本的分数等级。(The score level of the text is judged using the score text that has been used.)

说明：  自然语言处理模型的语料库，用于训练NLP模型(Corpus for training NLP model)

python语言写的博客网站，有良好的界面，完善的功能，实现了登陆注册，发布博客，查看博客，发布查看评论，管理员可以删除修改博客，删除评论。

说明：  用于图像去噪处理，使用ADM方法图像去噪处理器处理(Used for image denoising processing, using adm method image denoising processor processing)

做推荐系统及学习python可以用得到的，简单实用，顶顶顶

说明：  卷积网络正在推动着图像识别方面的进步，其不仅改进了整体图像的分类效果，而且在具有结构化输出的局部任务上也取得了进步，包括边界框目标检测，关键点预测等。 自然下一步是改进在像素级别上的预测。其实，以前的方法已经使用卷积网络进行语义分割任务，其中每个像素都被标记为属于目标或属于其他区域，但让具有缺点。 FCN和CNN的区别：CNN卷积层之后连接的是全连接层；FCN卷积层之后仍连接卷积层，输出的是与输入大小相同的特征图，提出一个端到端，像素对像素的全卷积网络用于语义分割任务(Convolution network is promoting the progress of image recognition. It not only improves the classification effect of the whole image, but also makes progress in the local tasks with structured output, including boundary box target detection, key point prediction and so on. The natural next step is to improve prediction at the pixel level. In fact, previous methods have used convolutional networks for semantic segmentation tasks, in which each pixel is marked as belonging to the target or other regions, but it has disadvantages. The difference between FCN and CNN: CNN convolution layer is connected with full connection layer after CNN convolution layer; FCN convolution layer is still connected with convolution layer after FCN convolution layer, and the output is the same as the input size of feature map. An end-to-end, pixel to pixel full convolution network is proposed for semantic segmentation task)

Python 基础教程 (莫烦 Python 教程)(Python Basic Tutorial)

说明：  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).)