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Neural networks and pattern recognition.pdf

Copyright © 1998 Elsevier Inc. All rights reserved Edited by: Omid Omidvar and Judith Dayhof
2011-11-15 上传大小:18.05MB
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机器学习,深度学习, Neural_Networks_for_Pattern_Recognition_-_Christopher_Bishop

最经典的机器学习,深度学习,材料,目前国内外下载量和引用率最高Neural_Networks_for_Pattern_Recognition_-_Christopher_Bishop

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Pattern Recognition and Neural Networks (B.D.Ripley).pdf

edit by ripley. university oxford

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Neural Networks for Pattern Recognition

Neural Networks for Pattern RecognitionNeural Networks for Pattern RecognitionNeural Networks for Pattern RecognitionNeural Networks for Pattern Recognition

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Pattern Recognition with neural networks in C++ (配套源码光盘)

这是书籍《神经网络模式识别及其实现-Pattern Recognition with neural networks in C++》,(美)Abhijit S. Pandya,Robert B. Macy著,徐勇,荆涛等译,的配套光盘,里面有书籍里面的C++代码,可以参考。我感觉书里的C++代码在IBM的os/2操作系统上实现,Windows系统下编译会有错误。不过里面的代码仍然值得参考。

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Pattern Recognition with Neural Networks in C++(C++在模式识别与神经网络应用)

分章节给出了Pattern Recognition with Neural Networks in C++(C++在模式识别与神经网络应用)的代码,对于初学者有较好的借鉴作用

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Bishop Pattern Recognition

模式识别领域最经典的一本算法书,748页完整版。

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Pattern recognition with neural networks in C++

Pattern recognition with neural networks in C++

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Pattern Recognition.pdf

Pattern Recognition.pdf

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[免费完整版]Neural Networks Tricks of the Trade

Neural Networks: Tricks of the Trade, Second Edition Editors: Grégoire Montavon, Geneviève B. Orr, Klaus-Robert Müller 有关神经网络、深度学习Tricks的入门经典书籍。

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Neural Networks and Deep Learning(中文版)

这本书最初是我学习 Neural Networks and Deep Learning 时做的中文笔记,因为原书中有很 多数学公式,所以我用 LATEX 来编写和排版,并将所有 LATEX 源码放置在 GitHub。其中部分内容 取自 Xiaohu Zhu 已经完成的翻译来避免重复的工作。 如果你对此中译本有任何建议和意⻅,欢迎以 issue 的方式提交到 GitHub 项目主⻚。 ——Freeman Zhang

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Deep Neural Networks for YouTube Recommendations论文翻译.pdf

Deep Neural Networks for YouTube Recommendations论文翻译

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Neural Networks and Learning Machines (高清.英文.书签.第三版).pdf

作者: Simon O. Haykin 出版社: Pearson 页数: 936 定价: USD 252.40 装帧: Hardcover ISBN: 9780131471399 作者简介 · · · · · · Simon Haykin 于1953年获得英国伯明翰大学博士学位,目前为加拿大McMaster大学电子与计算机工程系教授、通信研究实验室主任。他是国际电子电气工程界的著名学者,曾获得IEEE McNaughton金奖。他是加拿大皇家学会院士、IEEE会士,在神经网络、通信、自适应滤波器等领域成果颇丰,著有多部标准教材。 本书是关于神经网络的全面的、彻底的、可读性很强的、最新的论述。全书共15章,主要内容包括Rosenblatt感知器、通过回归建立模型、最小均方算法、多层感知器、核方法和径向基函数网络、支持向量机、正则化理论、主分量分析、自组织映射、信息论学习模型、动态规划、神经动力学、动态系统状态估计的贝叶斯滤波等。 本书适合作为高等院校计算机相关专业研究生及本科生的教材,也可供相关领域的工程技术人员参考。

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Neural Networks and Deep Learning-神经网络与深度学习

Michael Nielsen所著的Neural Networks and Deep Learning,非常适合用来入门神经网络和深度学习。原书为网页版书籍。这里提供PDF版本书籍。PDF版本制作者:欧拉。欧拉的博客:www.liuhao.me

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神经网络经典外文入门书籍

包含三本PDF书籍 Pattern classification using ensemble Methods.pdf Neural Networks Tricks of the Trade.pdf Neural Networks for Applied Sciences and Engineering.pdf

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Recurrent Convolutional Neural Networks for Text Classification

找到了国外的一篇文章《Recurrent Convolutional Neural Network For Text Classification》

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Neural Networks and Learning Machines

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

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Neural Network and Deep Learning高清中英文双版pdf

深度学习很好的入门书籍,高清版本pdf建议打印下来看,Neural Network and Deep Learning高清中英文双版

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Neural Networks: A Comprehensive Foundation (2nd Edition)

Simon Haykin的经典神经网络教程,英文原版,详尽讲解了神经网络的各个方面

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Neural Networks and Deep Learning: A Textbook

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

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Neural+Networks+and+Deep+Learning-神经网络与深度学习.pdf中文完整版

Neural+Networks+and+Deep+Learning-神经网络与深度学习.pdf中文完整版

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