Neural Networks and Learning Machines (3rd Edition).pdf 这本是全英文的文字版资源。大家如果学习machine learning 的话，建议自己看英文的，毕竟这东西国外比国内要先进得多，不能让英语成为障碍。而且，原版的东西绝对比翻译的要准确些，无论翻译的水平有多高。

2017 Deep Learning and Convolutional Neural Networks for Medical Image Computing.pdf

本书是Grégoire Montavon 2012年推出的第二版书，主要介绍神经网络的训练改进技巧、以及表示等等，本书高清无码扫描，附带完整标签，文字可编辑复制，并以保存为长期归档格式PDF/A！堪称完美！强烈推荐！

Recurrent Neural Networks for Prediction offers a new insight into the learning algorithms, architectures and stability of recurrent neural networks and, consequently, will have instant appeal. It provides an extensive background for researchers, academics and postgraduates enabling them to apply such networks in new applications.

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

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

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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.