PRML学习笔记之Neural Nerwork（神经网络）

完整工程案例：图像描述---Show and Tell: A Neural Image Caption Generator，基于Inception V3与LSTM实现图像描述，运行环境（Tensorflow1.0及以上，Python3.6）

完整工程案例：图像描述---Show and Tell: A Neural Image Caption Generator，基于Inception V3与LSTM实现图像描述，运行环境（Tensorflow1.0及以上，Python3.6）

Neural Network Methods for Natural Language Processing by Yoav Goldberg，上传的这个为英文版，方便大家看原版。中文版本为车万翔老师翻译的《基于深度学习的自然语言处理》

该压缩包包含了国外最新的Java神经网络教程，里面包含PDF教程与配套代码，供大家学习使用。

使用神经网络进行预测，有BF，FF，GRNN，RBF网络等， 使用神经网络进行预测 (MATLAB版)Neural Networks predict

Neural Network Design Demonstrations(神经网络设计代码演示) 一个神经网络源程序教学任务包，里面有130个M文件，可直接调用，供大家参考。

关于论文“Methods for interpreting and understanding deep neural networks”的学习摘要

matlab 神经网络 43讲，附带 修改版代码，有助于学习神经网络相关，希望降低下载积分谢谢

In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It contains an overview of neural network architectures for practical data analysis followed by extensive step-by-step coverage on linear networks, as well as, multi-layer perceptron for nonlinear prediction and classification explaining all stages of processing and model development illustrated through practical examples and case studies. Later chapters present an extensive coverage on Self Organizing Maps for nonlinear data clustering, recurrent networks for linear nonlinear time series forecasting, and other network types suitable for scientific data analysis. With an easy to understand format using extensive graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics. Features x Explains neural networks in a multi-disciplinary context x Uses extensive graphical illustrations to explain complex mathematical concepts for quick and easy understanding ? Examines in-depth neural networks for linear and nonlinear prediction, classification, clustering and forecasting x Illustrates all stages of model development and interpretation of results, including data preprocessing, data dimensionality reduction, input selection, model development and validation, model uncertainty assessment, sensitivity analyses on inputs, errors and model parameters Sandhya Samarasinghe obtained her MSc in Mechanical Engineering from Lumumba University in Russia and an MS and PhD in Engineering from Virginia Tech, USA.