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.

Neural Networks - A Comprehensive Foundation

使用神经网络进行预测，有BF，FF，GRNN，RBF网络等，

For those entering the field of artificial neural networks, there has been an acute need for an authoritative textbook that explains the main ideas clearly and consistently using the basic tools of linear algebra, calculus, and simple probability theory. There have been many attempts to provide such a text, but until now, none has succeeded. Some authors have failed to separate the basic ideas and principles from the soft and fuzzy intuitions that led to some of the models as well as to most of the exaggerated claims. Others have been unwilling to use the basic mathematical tools that are essential for a rigorous understanding of the material. Yet others have tried to cover too many different kinds of neural network without going into enough depth on any one of them. The most successful attempt to date has been "Introduction to the Theory of Neural Computation" by Hertz, Krogh and Palmer. Unfortunately, this book started life as a graduate course in statistical physics and it shows. So despite its many admirable qualities it is not ideal as a general textbook.

The wake-sleep algorithm for unsupervised neural networks 作者Hinton，提出Helmholtz机和wake-sleep算法

Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence，非常有用的资料

内容为时下最火热的神经网络和深度学习，该教程来源于美国Michael Nielsen的个人网站，他致力于把神经网络与深度学习的高深知识以浅显易懂的方式讲解出来，成为众多大牛推荐的必读网络资源之一。国内有识之士把其翻译成中文，方便了广大读者。是不可多得的优质资料！

开始使用MATLAB深入学习和AI深入底稿。在本书中，您将从机器学习基础开始，然后转向神经网络，深入学习，然后进行卷积神经网络。在基础和应用的基础上，MATLAB深度学习采用MATLAB作为本书中的示例和案例研究的底层编程语言和工具。 使用这本书，您将能够解决当今的一些现实世界的大数据，智能机器人和其他复杂的数据问题。您将看到，现代智能数据分析和使用，机器学习的复杂和更智能的方面学习有多深刻。 你会学到什么 使用MATLAB深入学习 发现神经网络和多层神经网络 使用卷积和池层 使用这些图层构建MNIST示例 这本书是谁 那些想用MATLAB深入学习的人。一些MATLAB的经验可能是有用的。

Michael Nielsen 的《Neural Networks and Deep Learning》 中文+英文+Python3代码，Michael Nielsen 的《Neural Networks and Deep Learning》 中文+英文+Python3代码

Neural Networks: Tricks of the Trade