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.

循环神经网络在语音识别中的应用 LSTM 双向RNN 双向lstm

复杂网络的matlab代码工具箱，Complex Networks Toolbox for MatLab

Michael Nielsen大神的巨作NNDL中文版，对深度学习基础讲解非常透彻

这是复现论文Learning to Compare Image Patches via Convolutional Neural Networks的代码，这是TensorFlow版本，用深度学习的方法做图像匹配，具体的过程可以看这篇文章https://blog.csdn.net/weixin_42521239/article/details/103389033

超图理论；无线通信网络中的应用 可以在spring查询一下目录。

Context-Dependent Pre-trained Deep Neural Networks for Large Vocabulary Speech Recognition

[译]SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks--翻译-附件资源

Large-Scale Learnable Graph Convolutional Networks（LGCN）论文的ppt pdf 资源分享