DeepRank Learning to rank with neural networks for recommendation.pdf

3D卷积说话人识别：用于说话人验证的深度学习和3D卷积神经网络

YouTube推荐系统Paper[2016]-Deep Neural Networks for YouTube Recommendations.pdf YouTube推荐系统Paper[2016]-Deep Neural Networks for YouTube Recommendations.pdf

AI NEUROSCIENCE: VISUALIZING AND UNDERSTANDING DEEP NEURAL NETWORKS

bp神经网络的自然语言实现，可完美运行，解决深度学习的任务，是一个非常的好多资源，能学习到很多东西，希望大家踊跃下载。。

VGG16 is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper “Very Deep Convolutional Networks for Large-Scale Image Recognition”. The model achieves 92.7% top-5 test accuracy in ImageNet, which is a dataset of over 14 million imag

Texture Synthesis Using Convolutional Neural Networks

文件中包含有BP神经网络拟合曲线函数实例的matlab代码，word文档一份内容为作业，一份内容为答案.

This book attempts to simplify and present the concepts of deep learning in a very comprehensive manner, with suitable, full-fledged examples of neural network architectures, such as Recurrent Neural Networks (RNNs) and Sequence to Sequence (seq2seq), for Natural Language Processing (NLP) tasks. The book tries to bridge the gap between the theoretical and the applicable. It proceeds from the theoretical to the practical in a progressive manner, first by presenting the fundamentals, followed by the underlying mathematics, and, finally, the implementation of relevant examples. The first three chapters cover the basics of NLP, starting with the most frequently used Python libraries, word vector representation, and then advanced algorithms like neural networks for textual data. The last two chapters focus entirely on implementation, dealing with sophisticated architectures like RNN, Long Short-Term Memory (LSTM) Networks, Seq2seq, etc., using the widely used Python tools TensorFlow and Keras. We have tried our best to follow a progressive approach, combining all the knowledge gathered to move on to building a questionand- answer system. The book offers a good starting point for people who want to get started in deep learning, with a focus on NLP. All the code presented in the book is available on GitHub, in the form of IPython notebooks and scripts, which allows readers to try out these examples and extend them in interesting, personal ways.