Create your own LoRa wireless projects for non-industrial use and gain a strong basic understanding of the LoRa technology, LoRa WAN, and LPWAN. You'll start by building your first LoRa wireless channel and then move on to various interesting projects such as setting up networks with a LoRa gateway, communicating with IoT servers using RESTful API and MQTT protocol, and real-time GPS tracking. With LoRa wireless and LoRaWAN, you can build a wide array of applications in the area of smart agriculture, smart cities, smart environment, smart healthcare, smart homes and buildings, smart industrial control, smart metering, smart supply chain and logistics. Beginning LoRa Radio Networks with Arduino provides a practical introduction and uses affordable and easy to obtain hardware to build projects with the Arduino development environment.

Convolutional Neural Networks 吴恩达 神经网络 WEEK3 最新版实验 2021 5月版

Feature Pyramid Networks for Object Detection.pdf

行为识别C3Dppt

Andrew.S.Tanenbaum 教授的计算机网络的英文版。

EDSR-Enhanced Deep Residual Networks for Single Image Super-Resolution,pytorch实现的，欢迎各位下载！

Michael Nielsen著，Neural Networks and Deep Learning 的中文翻译版，质量非常好！

MIMO Communication for Cellular Networks，2012年的一本新书，对SU-MIMO和MU-MIMO等做了详细的讲解，是一本难得的好书

EfficientNet论文

Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.