Basics of Linear Algebra for Machine Learning Discover the Mathematical Language of Data in Python

Basics of Linear Algebra for Machine Learning
Basics of Linear Algebra for Machine Learning: Discover the Mathematical Language of Data in Python
by: Jason Brownlee
Release Finelybook 出版日期: 2018
pages 页数: 212
Language 语言: English
pages 页数: PDF
Basics of Linear Algebra Contents
Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it.
In this new laser-focused Ebook written in the friendly Machine Learning Mastery style that you’re used to,you will finally cut through the equations,Greek letters,and confusion,and discover the topics in linear algebra that you need to know.
Using clear explanations,standard Python libraries,and step-by-step tutorial lessons,you will discover what linear algebra is,the importance of linear algebra to machine learning,vector,and matrix operations,matrix factorization,principal component analysis,and much more.
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,operations,and techniques in linear algebra that are directly relevant to machine learning algorithms.
There are a lot of things you could learn about linear algebra,from theory to abstract concepts to APIs. My goal is to take you straight to developing an intuition for the elements you must understand with laser-focused tutorials.
I designed the tutorials to focus on how to get things done with linear algebra. They give you the tools to both rapidly understand and apply each technique or operation.
Each tutorial is designed to take you about one hour to read through and complete,excluding the extensions and further reading.
You can choose to work through the lessons one per day,one per week,or at your own pace. I think momentum is critically important,and this book is intended to be read and used,not to sit idle.
I would recommend picking a schedule and sticking to it.
The tutorials are divided into five parts:
Foundation. Discover a gentle introduction to the field of linear algebra and the relationship it has with the field of machine learning.
NumPy. Discover NumPy tutorials that show you how to create,index,slice,and reshape NumPy arrays,the main data structure used in machine learning and the basis for linear algebra examples in this book.
Matrices. Discover the key structures for holding and manipulating data in linear algebra in vectors,matrices,and tensors.
Factorization. Discover a suite of methods for decomposing a matrix into its constituent elements in order to make numerical operations more efficient and more stable.
Statistics. Discover statistics through the lens of linear algebra and its application to principal component analysis and linear regression.


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