演示代码（请参阅jupyter笔记本）： 使用深度卷积自动编码器对地震信号进行非监督（自我监督）区分 您可以从这里获取论文： 连结1： 连结2： 您可以从此处获取训练数据集： 参考： Mousavi, S. M., W. Zhu, W. Ellsworth, G. Beroza (2019). Unsupervised Clustering of Seismic Signals Using Deep Convolutional Autoencoders, IEEE Geoscience and Remote Sensing Letters, 1 - 5, doi:10.1109/LGRS.2019.2909218.

这也是一篇关于关于网络表述学习的综述，国外一篇很具代表性的survey，

Boost 1.72.0版本 Asio文档的中文翻译，中英文对照，PDF格式。 对应英文文档为[Boost.Asio](https://www.boost.org/doc/libs/1_72_0/doc/html/boost_asio.html)。 翻译了“Overview”、“Using Boost.Asio”、“Tutorial”、“Examples”的完整章节。 [“Networking TS compatibility”](https://www.boost.org/doc/libs/1_72_0/doc/html/boost_asio/net_ts.html)的第二个表格未翻译。 其他“Reference”、“Revision History”、“Index”章节未翻译。

교과서3판 2019년5月출간，출판사 Se Se Se（Sebastian Raschka）미자리리리（Vahid Mirjalili）셀러베트스베셀러“ ” 。 주세요이나오류가있다블면이그블로그블로 알려주세요주세요주세요주세요 주세요주세요주세요 교과서1저장소는다음과다（1판이판에다2）。 노트북 도움말은 에장의장의 을을 하세요。 open_dir 폴더로이동합니다。 또는 ipynb 바로바있습니다있습니다。 nbviewer 뷰어로뷰어링크입니다。 colab （Colab）링크입니다。 [이터에서배운다[] [ ] [ ] [ ] open

在本文中，我简要介绍了ONNX运行时和ONNX格式。

oco-2-数据网络 NASA OCO-2的数据代理和API 检查 检查 Data is from NASA's OCO-2

本书全面介绍了经典的和现代的网络流技术，包括综合的理论、算法与应用。主要内容包括：路径、树与周期，算法设计与分析，最大流与最小流算法，分派与匹配，最小生成树，拉格朗日松弛与网络优化等。书中包含大量练习题，拓展了本书的内容，便于教学。 　　 本书特点： 　　 深入介绍功能强大的算法策略和分析工具，如数据缩放和势函数变量。 　　 讨论有关网络优化的重要主题及实际解决方案，如拉格朗日松弛法。 　　 包括广泛的文献注解，提供宝贵的历史背景和指导。 　　 包含800多道难度不一的练习题。

吴恩达在coursera上深度学习第一课Neural Network and Deep Learning的课后编程答案，作业是用python写的，大家一起深度学习吧

This book is for people who want to write programs that communicate with each other using an application program interface (API) known as sockets. Some readers may be very familiar with sockets already, as that model has become synonymous with network programming. Others may need an introduction to sockets from the ground up. The goal of this book is to offer guidance on network programming for beginners as well as professionals, for those developing new network-aware applications as well as those maintaining existing code, and for people who simply want to understand how the networking components of their system function.

MVCNN-PyTorch Multi-View CNN built on ResNet/AlexNet to classify 3D objects A PyTorch implementation of MVCNN using ResNet, inspired by the paper by . MVCNN uses multiple 2D images of 3D objects to classify them. You can use the provided dataset or create your own. Also check out my implementation whitch outperforms MVCNN (Under construction). Dependencies torch torchvision numpy tensorflow (for