实用机器学习

Mathematics for Machine Learning

Mathematics for Machine Learning

Machine_Learning_Notes

Machine_Learning_Notes

Introduction to Machine Learning Notes

English Version of Introduction to Machine Learning Notes

Introductory Machine Learning Notes

MIT course -- Introduction to ML.

Mathematics for Machine Learning【Marc Peter Deisenroth】

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but ...

mathematics for machine learning

mathematics for machine learning draft

《Mathematics for Machine Learning》读书笔记（一）1

《Mathematics for Machine Learning》读书笔记（一）一、线性代数1．核心概念及相互关系下图摘抄自书中第二章开篇之处，较详细的展示了

国外开源资料 MATHEMATICS FOR MACHINE LEARNING

2020国外开源资料 MATHEMATICS FOR MACHINE LEARNING 纯英文 介意勿下 适合人工智能从业者、高校学生等 包括线代、概率论、最优化等内容

Database之Applied Mathematics for Database Professionals.pdf

Database之Applied Mathematics for Database Professionals.pdf

Applied-Machine-Learning

应用机器学习 练习题 专案

EE-695-Applied-Machine-Learning

EE-695-应用机器学习 “应用机器学习”课程的作业和项目的综合日志 最终项目： 野外追踪1M +学生的知识状态 最终项目 最终项目 原始

Applied Genetic Programming and Machine Learning

This book delivers theoretical and practical knowledge on extension of Genetic Programming (GP) for practical applications. It provides a methodology for integrating Genetic Programming and machine-learning techniques. The de- velopment of such tools contributes to the establishment of a more robust evolutionary framework when addressing tasks from such areas as chaotic time-series prediction, system identification, financial forecasting, classifica- tion, and data mining.

Machine Learning Projects for .NET Developers(Apress,2015)

While some machine learning algorithms use fairly advanced mathematics, this book focuses on simple but effective approaches. If you enjoy hacking code and data, this book is for you.

《mathematics for machine learning》

本身是介绍机器学习中用到的数学知识

Applied Mathematics for Database Professionals

Database Mathematcis

Basics of Linear Algebra for Machine Learning

This book was designed to be a crash course in linear algebra for machine learning practitioners. Ideally, those with a background as a developer. This book was designed around major data structures, ...

Basics of Linear Algebra for Machine Learning (Python)

Basics of Linear Algebra for Machine Learning: Discover the Mathematical Language of Data in Python By 作者: Jason Brownlee Pub Date: 2018 ISBN: n/a Pages: 212 Language: English Format: PDF Linear ...

Scala Applied Machine Learning epub

Scala Applied Machine Learning 英文epub 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

Applied Machine Learning (2019).pdf

The book is revised and corrected from class notes written for a course I've taught on numerous occasions to very broad audiences. Most students were at the final year undergraduate/first year graduate student level in a US university. About half of each class consisted of students who weren’t computer science students but still needed a background in learning methods. The course stressed applying a wide range of methods to real datasets, and the book does so, too.

Some Notes about Support Vector Machine

Some Notes about Support Vector Machine

Scala:Applied Machine Learning

Scala:Applied Machine Learning by Pascal Bugnion English | 23 Feb. 2017 | ISBN-13: 9781787126640 | 1843 Pages | EPUB/PDF (conv) | 33.15 MB Leverage the power of Scala and master the art of building, improving, and validating scalable machine learning and AI applications using Scala's most advanced and finest features. About This Book Build functional, type-safe routines to interact with relational and NoSQL databases with the help of the tutorials and examples provided Leverage your expertise in Scala programming to create and customize your own scalable machine learning algorithms Experiment with different techniques; evaluate their benefits and limitations using real-world financial applications Get to know the best practices to incorporate new Big Data machine learning in your data-driven enterprise and gain future scalability and maintainability Who This Book Is For This Learning Path is for engineers and scientists who are familiar with Scala and want to learn how to create, validate, and apply machine learning algorithms. It will also benefit software developers with a background in Scala programming who want to apply machine learning. What You Will Learn Create Scala web applications that couple with JavaScript libraries such as D3 to create compelling interactive visualizations Deploy scalable parallel applications using Apache Spark, loading data from HDFS or Hive Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters Apply key learning strategies to perform technical analysis of financial markets Understand the principles of supervised and unsupervised learning in machine learning Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet Construct reliable and robust data pipelines and manage data in a data-driven enterprise Implement scalable model monitoring and alerts with Scala In Detail This Learning Path aims to put the entire world of machine learning with Scala in fron

MIT关于machine learning 的所有习题和答案

上传的是关于MIT的machine learning课程中的所有的习题和答案，讲得非常的详细，希望对大家有帮助

Introduction to applied mathematics

很经典 有时间还是要好好读一下的 （我一有时间准备看第二遍）

Machine Learning for Decision Makers-Apress(2017).pdf

Machine learning is the next most important movement of innovation, which is guided by developments in computing power and based on the solid foundation of mathematics. Its capability of accumulation...

python大作业 含爬虫、数据可视化、地图、报告、及源码（整和为一个文件）（2014-2020全国各地区原油加工量）.rar

（含源码及报告）本程序分析了自2014年到2020年每年我国原油加工的产量，并且分析了2019年全国各地区原油加工量，含饼状图，柱状图，折线图，数据在地图上显示。运行本程序需要requests、bs4、csv、pandas、matplotlib、pyecharts库的支持，如果缺少某库请自行安装后再运行。文件含2个excel表，4个csv文件以及一个名字为render的html文件（需要用浏览器打开），直观的数据处理部分是图片以及html文件，数据处理的是excel文件。不懂可以扫文件中二维码在微信里面问。

仿真电路以及操作方法

用一片通用四运放芯片LM324组成电路，实现以下功能：用低频信号源产生ui1=0.1sin2πft（V），f=500Hz的正弦波信号，加至加法器的输入端，加法器的另输入端加入有自制振荡器产生的信号uo1。要求加法器的输出电压ui2=10 ui1+ uo1。ui2经选频滤波器滤除uo1频率分量，选出f信号为uo2，uo2为峰峰值等于9V的正弦信号。uo2信号经比较器后在1KΩ负载上得到峰峰值2V的输出电压uo3。用NI Multisim 打开即可，参数已调好。对应博客：https://blog.csdn.net/weixin_43723423/article/details/90761331