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.

简单遗传编程 对于符号回归 此Python 3代码是用于符号回归的遗传编程的简单实现，并且已出于教育目的而开发。 依存关系 numpy和sklearn 。 文件test.py显示了用法示例。 安装 您可以使用python3 -m pip install --user simplegp通过python3 -m pip install --user simplegp ，也可以通过下载代码并运行python3 setup.py install --user在本地进行python3 setup.py install --user 。 参考 如果您使用此代码，请通过引用（或为此）代码所针对的我们的一部或多部作品来支持我们的研究： M. Virgolin，A。De Lorenzo，E。Medvet，F。Randone。 “学习可解释性的公式以学习可解释的公式”。 ，施普林格（2020）。 （ ）

dm_env ：DeepMind RL环境API 该软件包描述了用于Python强化学习（RL）环境的界面。 它由以下核心组件组成： dm_env.Environment ：RL环境的抽象基类。 dm_env.TimeStep ：一个容器类，表示每个时间步（过渡）上环境的输出。 dm_env.specs ：一个模块，包含用于描述环境消耗的动作的格式以及其返回的观察值，奖励和折扣的原语。 dm_env.test_utils ：用于测试具体环境实现是否符合dm_env.Environment接口的工具。 请参阅的文档以获取有关环境接口的语义以及如何使用它的更多信息。 子目录还包含使用dm_env接口实现的RL环境的说明性示例。 安装 dm_env可以使用pip从PyPI安装： pip install dm-env 请注意，从1.4版开始，我们仅支持Python 3.6+。 您还

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machine leaning 谷歌课程

GROBID GROBID文档 请访问以获取更多详细信息。 概要 GROBID（或Grobid，但不是GroBid或GroBiD）表示书目数据的生成。 GROBID是一个机器学习库，用于将原始文档（例如PDF）提取，解析和重组为结构化XML / TEI编码的文档，尤其侧重于技术和科学出版物。 最早的发展始于2008年，是一种业余爱好。 在2011年，该工具已以开源形式提供。 自开始以来，作为副项目的GROBID工作就一直稳定，并有望继续进行。 可以使用以下功能： 从PDF格式的文章中提取标题并进行解析。 这里的摘录涵盖了通常的书目信息（例如标题，摘要，作者，隶属关系，关键字等）。 从.