NoC的倡导者Benini的书，书太大了，分两个部分上传的。

一本专门针对大规模复杂网络动力学系统研究的书，亚马逊全5分评价。

Google的deepmind团队发表在nature上有关alphago的论文，包含原有的英文版，我翻译的中文版，以及一个20分钟对alphago工作原理的讲述。

开发神经元网络的丰富俱乐部拓扑结构 论文“富俱乐部拓扑结构的出现和海马功能网络体外发育中的协调动力学”的补充材料。

Tensorflow中的Grad-CAM实施 此回购协议是Gradient类激活图（Grad-CAM [1]）的TensorFlow实现，这是深度学习网络的可视化技术之一。 此仓库基于Grad-CAM的和版本。 要求 Python3.x Tensorflow 1.x （包括经过预训练的（使用Imagenet数据集）VGG16分类模型文件VGG16.npy （有关如何下载的信息，请参阅自述文件）） 用法 python grad-cam-tf.py [top_n] path_to_image ：为其计算Grad-CAM的图像。 path_to_VGG16_npy ： 提供的训练VGG16模型数据 top_n ：可选。 为每个“ top_n”类计算Grad-CAM，这由VGG16预测。 以下图像与pa

Supervised Sequence Labelling with Recurrent Neural Networks,Supervised Sequence Labelling with Recurrent Neural Networks,Supervised Sequence Labelling with Recurrent Neural Networks,

高效且可扩展的物理信息深度学习 搭配为主PINN求解器和PDE发现方法之上分布式计算多工人。 如果需要，请使用TensorDiffEq： 一个无网格的PINN求解器，可以分布在多个工作程序（GPU）上以解决正向问题（推理）和逆向问题（发现） 可扩展域-迭代求解器构造允许ND时空支持包括对不带时间元素的ND空间域的支持 正向和反向PINN的自适应配置方法 直观的用户界面，可对变量域，边界条件，初始条件和强格式PDE进行明确定义 是什么让TensorDiffEq与众不同？ 完全开源 求解可解决正向和反向问题，从而提高了解决方案的准确性和培训的稳定性，从而减少了总体培训时间 适用于大型或细粒度时空域的多GPU分布式训练 建立在Tensorflow 2.0之上，以增加对最新TF版本独有的新功能的支持，例如，有效图形构建的以及图形优化的*-源代码不可能再被淘汰Tensorflow版本发行 直

The Remote Device Management Protocol (RDM) permits intelligent bi-directional communication between devices from multiple manufacturers utilizing a modified DMX512 data link. RDM is an EF 1.0 implementation of ANSI E1.11. RDM permits a console or other controlling device to discover and then configure, monitor, and manage intermediate and end-devices connected through a DMX512 network. RDM provides for intelligent control of devices on a DMX512 network, which has not been previously available outside of proprietary networks. This standard specifies: the physical layer and timings, device discovery process and algorithms, message structure and communication.

Neural Networks and Deep Learning 中文版 Michael Nielsen 著 完整书签 很好的入门神经网络和深度学习的书籍！

复杂网络的经典外文著作《Complex Networks Principles Methods and Applications》 Networks constitute the backbone of complex systems, from the human brain to computer communications, transport infrastructures to online social systems, metabolic reactions to financial markets. Characterising their structure improves our understanding of the physical, biological, economic and social phenomena that shape our world. Rigorous and thorough, this textbook presents a detailed overview of the new theory and methods of network science. Covering algorithms for graph exploration, node ranking and network generation, among the others, the book allows students to experiment with network models and real-world data sets, providing them with a deep understanding of the basics of network theory and its practical applications. Systems of growing complexity are examined in detail, challenging students to increase their level of skill. An engaging pre- sentation of the important principles of network science makes this the perfect reference for researchers and undergraduate and graduate students in physics, mathematics, engineering, biology, neuroscience and social sciences.