【免费下载】 MIT18.065 线性代数与数据学习资源下载

MIT18.065 线性代数与数据学习资源下载

【下载地址】MIT18.065线性代数与数据学习资源下载分享 “MIT18.065 Linear Algebra and Learning from Data”是 Gilbert Strang 教授的得力之作,旨在帮助学习者深入理解线性代数在数据学习中的应用。该资源不仅包含了线性代数的基础知识,还结合了现代数据学习的实际案例,使学习者能够将理论知识与实际应用相结合 【下载地址】MIT18.065线性代数与数据学习资源下载分享 项目地址: https://gitcode.com/Open-source-documentation-tutorial/e7cb4

本仓库提供了一个资源文件的下载,该资源文件名为“MIT18.065 Linear Algebra and Learning from Data”。这份资源是由著名教授 Gilbert Strang 精心编写的,内容涵盖了线性代数与数据学习的核心概念与应用。

资源描述

“MIT18.065 Linear Algebra and Learning from Data”是 Gilbert Strang 教授的得力之作,旨在帮助学习者深入理解线性代数在数据学习中的应用。该资源不仅包含了线性代数的基础知识,还结合了现代数据学习的实际案例,使学习者能够将理论知识与实际应用相结合。

适用人群

  • 对线性代数感兴趣的学生和研究者
  • 希望深入了解线性代数在数据学习中应用的专业人士
  • 需要复习或巩固线性代数知识的工程师和科学家

如何使用

  1. 点击仓库中的下载链接,获取资源文件。
  2. 使用支持的阅读器打开文件,开始学习。
  3. 结合实际案例,深入理解线性代数与数据学习的结合应用。

注意事项

  • 请确保您的设备支持所下载文件的格式。
  • 建议在学习过程中结合其他相关资源,以获得更全面的知识体系。

希望这份资源能够帮助您在学习和研究的道路上取得更大的进步!

【下载地址】MIT18.065线性代数与数据学习资源下载分享 “MIT18.065 Linear Algebra and Learning from Data”是 Gilbert Strang 教授的得力之作,旨在帮助学习者深入理解线性代数在数据学习中的应用。该资源不仅包含了线性代数的基础知识,还结合了现代数据学习的实际案例,使学习者能够将理论知识与实际应用相结合 【下载地址】MIT18.065线性代数与数据学习资源下载分享 项目地址: https://gitcode.com/Open-source-documentation-tutorial/e7cb4

创作声明:本文部分内容由AI辅助生成(AIGC),仅供参考

Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.
Preface I wrote this book to help machine learning practitioners, like you, get on top of linear algebra, fast. Linear Algebra Is Important in Machine Learning There is no doubt that linear algebra is important in machine learning. Linear algebra is the mathematics of data. It’s all vectors and matrices of numbers. Modern statistics is described using the notation of linear algebra and modern statistical methods harness the tools of linear algebra. Modern machine learning methods are described the same way, using the notations and tools drawn directly from linear algebra. Even some classical methods used in the field, such as linear regression via linear least squares and singular-value decomposition, are linear algebra methods, and other methods, such as principal component analysis, were born from the marriage of linear algebra and statistics. To read and understand machine learning, you must be able to read and understand linear algebra. Practitioners Study Linear Algebra Too Early If you ask how to get started in machine learning, you will very likely be told to start with linear algebra. We know that knowledge of linear algebra is critically important, but it does not have to be the place to start. Learning linear algebra first, then calculus, probability, statistics, and eventually machine learning theory is a long and slow bottom-up path. A better fit for developers is to start with systematic procedures that get results, and work back to the deeper understanding of theory, using working results as a context. I call this the top-down or results-first approach to machine learning, and linear algebra is not the first step, but perhaps the second or third. Practitioners Study Too Much Linear Algebra When practitioners do circle back to study linear algebra, they learn far more of the field than is required for or relevant to machine learning. Linear algebra is a large field of study that has tendrils into engineering, physics and quantum physics. There are also theorems and derivations for nearly everything, most of which will not help you get better skill from or a deeper understanding of your machine learning model. Only a specific subset of linear algebra is required, though you can always go deeper once you have the basics.
### 关于线性代数数据分析的中文资料 对于希望深入了解线性代数并将其应用于数据分析的学习者而言,存在多种高质量的中文书籍、教程和其他资源可供选择。 #### 中文书籍推荐 - **《线性代数及其应用》**:由 Gilbert Strang 所著,此书已被翻译成多国语言版本,其中包括中文版。书中不仅涵盖了传统意义上的线性代数理论,而且特别强调了其在工程和技术领域内的具体应用场景[^2]。 - **《统计学习导论——基于R的应用》**:虽然主要关注的是统计学方面,但这本书同样涉及到了大量有关如何运用线性代数来进行有效的数据建模的内容,并配有丰富的实例说明和练习题,非常适合想要提升自己编程能力的同时掌握更多实用技能的人士阅读。 #### 在线教程其他资源链接 - **Coursera 和 edX 平台上的课程**:这两个平台提供了一系列优质的在线教育服务,其中不乏专注于线性代数及其实战技巧的专项训练营或系列讲座形式的教学视频;部分课程甚至会专门针对中国学生开设中文授课选项[^1]。 - **Jupyter Notebook 实践指南**:作为一种交互式的笔记本环境,Jupyter 支持多种主流编程语言(如Python),允许用户在同一文档内完成代码编辑、运行测试以及结果展示等一系列操作流程。通过查阅官方文档或是参社区讨论区交流心得体验等方式,初学者们可以更快地上手实践各种复杂的数学运算过程[^3]。 - **GitCode 上开源项目仓库**:这里汇集了许多开发者贡献出来的优质材料,比如“MIT18.065 线性代数数据学习”的配套讲义就托管在此处供大众免费获取下载使用。 ```python import numpy as np from scipy import linalg # 创建两个随机矩阵 A 和 B A = np.random.rand(4, 4) B = np.random.rand(4, 4) # 计算 AB 的乘积 C C = np.dot(A, B) print("Matrix multiplication result:\n", C) # 对角化矩阵 D (假设它是可对角化的) D = np.array([[1., 2.], [3., 4.]]) eigenvalues, eigenvectors = linalg.eig(D) print("\nEigenvalue decomposition results:") print("Eigenvalues:", eigenvalues) print("Eigenvectors:\n", eigenvectors) ```
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包

打赏作者

缪超争Lighthearted

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付:¥1
获取中
扫码支付

您的余额不足,请更换扫码支付或充值

打赏作者

实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值