pcm音频转换成opus的Python代码

程序主要是基于ADBOOST的分类算法，编程软件是python，实现的功能是在某个数据集上的应用，数据集分为训练数据和测试数据

说明：  蒙特卡洛编程，含程序代码，代码直接复制即可，还有实例！！！(Monte Carlo programming, including program code.)

说明：  对于深度学习中的图神经网络中图卷积网络的实战代码(For the practical code of graph convolution network in graph neural network)

基于Socket的简单局域网聊天程序，使用Python编写(Socket Chat)

说明：  使用NMF识别，输入样本矩阵，进行非负矩阵分解得到分解矩阵。(Nonnegative matrix decomposition using NMF identification)

一个用python实现的PCA算法，并且给了简易素材(A PCA algorithm implemented in python, and gave a simple material)

cnn 分类 一维 信号分类 模型简单适合新手，效果一般(Cnn classification One-dimensional signal classification The model is simple for beginners, the effect is average)

我们提供一类称为MobileNets的高效模型，用于移动和嵌入式视觉应用。?MobileNets是基于一个流线型的架构，它使用深度可分离的卷积来构建轻量级的深层神经网络。我们引入两个简单的全局超参数，在延迟度和准确度之间有效地进行平衡。这两个超参数允许模型构建者根据问题的约束条件，为其应用选择合适大小的模型。我们进行了资源和精度权衡的广泛实验，与ImageNet分类上的其他流行的网络模型相比，MobileNets表现出很强的性能。最后，我们展示了MobileNets在广泛的应用场景中的有效性，包括物体检测，细粒度分类，人脸属性和大规模地理定位。(We provide an efficient model called MobileNets for mobile and embedded vision applications. MobileNets is based on a streamlined architecture that USES deep separable convolution to build a lightweight deep neural network. We introduce two simple global hyperparameters to effectively balance the delay and accuracy. These two hyperparameters allow the model builder to select an appropriate size model for its application based on the constraints of the problem. We conducted extensive experiments on resource and precision tradeoffs, and MobileNets showed strong performance compared with other popular network models on the ImageNet classification. Finally, we demonstrate the effectiveness of MobileNets in a wide range of application scenarios, including object detection, fine-grained classification, face attributes, and large-scale geographic localization.)

这两篇文章为传感器优化配置方面的资料，个人觉得比较有用，希望可以帮到需要的人(These two articles provide information on the optimal configuration of the sensor)