经典的模式识别与机器学习教材的课后习题Pattern Recognition and Machine Learning习题答案

Scratch素材大龙老师115

在这些教程中，我们将演示和可视化遗传算法，进化策略，NEAT等算法。 下文提到的所有方法都有其中文视频和文本教程。 请访问 。 捐款 如果这样做对您有帮助，请考虑捐赠以支持我以获得更好的教程！ 任何贡献都将不胜感激！

CodeSnippetSearch CodeSnippetSearch是一个Web应用程序和一个Web扩展，允许您使用自然语言查询和代码本身搜索GitHub存储库。 它基于使用PyTorch和项目中的数据的单词代码搜索实现的神经袋。 模型培训代码受到CodeSearchNet存储库中基线（Tensorflow）实现的极大启发。 当前，支持Python，Java，Go，Php，Javascript和Ruby编程语言。 有用的论文： 型号说明 模型结构 项目结构 code_search ：一个带有脚本的Python包，用于准备数据，训练语言模型并保存嵌入 code_search_web ：CodeSnippetSearch网站Django项目 serialized_data ：在训练期间存储中间对象（文档，词汇表，模型，嵌入等） codesearchnet_data ：来自CodeSe

Rebiber：使用官方信息标准化bibtex的工具。 我们经常引用使用他们的arXiv的论文版本不提的是，他们在一些会议已经发布。 这些非正式的围兜条目可能会违反某些会议的提交规则或适用于摄像头的版本规则。 我们引入Rebiber ，这是Python中的一个简单工具，可以自动修复它们。 它基于来自或的官方会议信息（适用于NLP会议）！ 您可以在查看支持的会议列表。 您可以用作简单的网络演示。 安装 pip install rebiber -U 要么 git clone https://github.com/yuchenlin/rebiber.git cd rebiber/ pip in

机器学习 05.Advice for applying machine learning 编程作业 jupyter note版本 机器学习与数据挖掘 machine learning

OpenNMT-py：开源神经机器翻译 OpenNMT-py是项目的版本， 项目是一个开源（MIT）神经机器翻译框架。 它被设计为易于研究的，可以尝试翻译，摘要，形态和许多其他领域的新思想。 一些公司已经证明该代码可以投入生产。 我们喜欢捐款！ 请查看带有标签的问题。 提出问题之前，请确保您已阅读要求和文档示例。 除非有错误，否则请使用或提出问题。 公告-OpenNMT-py 2.0 我们很高兴宣布即将发布OpenNMT-py v2.0。 此版本背后的主要思想是-几乎完整地改造了数据加载管道。 引入了新的“动态”范式，允许对数据进行动态转换。 这具有一些优点，其中包括： 删除或

Understanding Machine Learning - From Theory to Algorithms这本书的中文扫描版

Machine learning is becoming important in every discipline. It is used in engineering for autonomous cars. It is used in finance for predicting the stock market. Medical professionals use it for diagnoses. While many excellent packages are available from commercial sources and open-source repositories, it is valuable to understand how these algorithms work. Writing your own algorithms is valuable both because it gives you insight into the commercial and open-source packages and also because it gives you the background to write your own custom Machine Learning software specialized for your application. MATLAB® had its origins for that very reason. Scientists who needed to do operations on matrices used numerical software written in FORTRAN. At the time, using computer languages required the user to go through the write-compile-link-execute process that was time consuming and error prone. MATLAB presented the user with a scripting language that allowed the user to solve many problems with a few lines of a script that executed instantaneously. MATLAB has built-in visualization tools that helped the user better understand the results. Writing MATLAB was a lot more productive and fun than writing FORTRAN. The goal of MATLAB Machine Learning is to help all users harness the power of MATLAB to do a wide range of learning problems. This book has two parts. The first part, Chapters 1–3, provides background on machine learning including learning control that is not often associated with machine intelligence. We coin the term “autonomous learning” to embrace all of these disciplines. The second part of the book, Chapters 4–12, shows complete MATLAB machine learning applications. Chapters 4–6 introduce the MATLAB features that make it easy to implement machine learning. The remaining chapters give examples. Each chapter provides the technical background for the topic and ideas on how you can implement the learning algorithm. Each example is implemented in a MATLAB script supported by a number of MATLAB functions. The book has something for everyone interested in machine learning. It also has material that will allow people with interest in other technology areas to see how machine learning, and MATLAB, can help them solve problems in their areas of expertise.

Apress出版, 2019年的书。全英文。我还没看，无法发表意见。请自己到Amazon看介绍.