pattern recognition and machine learning的翻译版本，不到500页，不如原版的749页那么多。

尽管Bishop的行文很优美，有时候简直可以拿出来当speech背诵，但是对于理论的理解还是母语比较稳妥和踏实。

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications. This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

Pattern Recognition and Machine Learning，英文原版，非扫描版， 高清晰度，Machine Learning 大牛Bishop著

数学AI 输入一张包含数学计算题的图片，输出识别出的数学计算式以及计算结果。请查看系统文档说明来运行程序。注意，这是一个半开源的项目，目前上传的版本只能处理简单的一维加减乘除算术表达式（如果想要识别更复杂的表达式，可以参考数学公式识别的论文）。可以参考的代码是前面的字符识别部分以及整个算法处理框架。 整个程序使用python实现，具体处理流程包括图像预处理，字符识别，数学公式识别，数学公式语义理解，结果输出。 我在TensorFlow上实现了一个lenet5的卷积神经网络识别数学字符，训练使用CHROME数据集。对于数学公式的识别，主要是将识别出的独立的字符组织成计算机能够理解的数学公式（这

matlab精度检验代码自述文件 该存储库列出了用于开发尖峰神经网络的文件，这些文件用于基于MNIST数据集的手写数字分类的基于监督学习的应用程序。 以类似于随机梯度下降的方式训练网络，其中权重在图像的每次显示结束时更新。 SNN中使用的神经元是简单的泄漏积分并触发神经元。 本文描述了NormAD的监督SNN训练算法：N. Anwani和B. Rajendran，“ NormAD-基于尖峰神经元的标准化近似后裔监督学习规则”，2015年国际神经网络联合会议（IJCNN），基拉尼，2015年，第1-8页。 本文描述了使用NormAD算法的三层SNN的CUDA实现。 如果您在工作中使用我们的代码，请引用以下内容。 SR Kulkarni，JM Alexiades和B. Rajendran，“具有尖峰神经网络的手写数字的学习和实时分类”，2017年第24届IEEE电子，电路和系统国际会议（ICECS），巴统，2017年，第128页。 -131。 doi：10.1109 / ICECS.2017.8292015 URL： Arxiv链接位于：SR Kulkarni，J。Alexiades和B.

关于模式识别的电子书，第三版，经典著作，总页数为668页

一个是jian xiao的Notes on Pattern Recognition and Machine Learning (Bishop)，一个是prml读书会的讲义。都是学习prml的资料

edit by ripley. university oxford

《机器学习与模式识别》同作者的又一经典之作，本书可以作为模式识别、神经网络的学习教材和参考书籍。本书为英文PDF版本，下载解压即可。