三星ML-1670_ML1676_ML1675清零软件_V04 解码软件 解决红灯不打印的问题 一次解码永久加粉不用换芯片 不用换鼓 解码说明： 首先电脑只接一台打印机，电脑能检测到打印机状态，刷机前先把打印机关机重新开机，预热完毕后就可以开始刷机解码了。

以MovieLens 的 ml-100k 为实验数据，基于 ItemCF 算法的推荐结果。

以MovieLens 的 ml-100k 为实验数据，基于 ItemCF 算法作推荐，实现代码。

MLKit示例 一组快速入门示例，这些示例演示了Android和iOS上的 API。 注意：由于此仓库的工作方式，我们不再直接接受拉取请求。 相反，我们将在内部对其进行修补，然后将其同步出去。

两配对样本, 麦克尼马尔检验(McNemar test)等. 介绍可查看: https://blog.csdn.net/orDream/article/details/122540099

预测机票价格的Web应用程序

Vivado ML 2021 百度网盘链接.txt

spring1718-Assignment3 --cs231n's the newest source code and learning source

机器学习PPT模板 [ML Visuals](https://docs.google.com/presentation/d/11mR1nkIR9fbHegFkcFq8z9oDQ5sjv8E3JJp1LfLGKuk/edit?usp=sharing) is a new collaborative effort to help the machine learning community in improving science communication by providing free professional, compelling and adequate visuals and figures. Currently, we have over 100 figures (all open community contributions). You are free to use the visuals in your machine learning presentations or blog posts. You don’t need to ask permission to use any of the visuals but it will be nice if you can provide credit to the designer/author (author information found in the slide notes). Check out the versions of the visuals below. This is a project made by the [dair.ai](https://dair.ai/) community. The latest version of the Google slides can be found in this GitHub repository. Our community members will continue to add more common figures and basic elements in upcoming versions. Think of this as free and open artifacts and templates which you can freely and easily download, copy, distribute, reuse and customize to your own needs. ML Visuals is now being used to power 100s of figures used by master/PhD students, papers (like this [one](https://arxiv.org/abs/2010.05113)), among other use cases.

作为深度学习领域的破冰之作,BP神经网络重新燃起了人们对深度学习的热情.它解决了DNN中的隐层传递中的权重值的计算问题.那么,BP算法思想是什么?它又是如何实现的呢?这就是本文的研究内容.