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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 ove r 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.
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2016-02-15 上传大小:5.24MB
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Learning From Data_Yaser

机器学习的基础,从贝耶斯理论等基础出发,对各种机器学习的基本理论特别是深度学习的原理进行了分析,是一本较好的机器学习教材

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《learning from data》读书笔记---第三章: The Linear Model
Learning from Data (Yaser)

機器學習基石推薦教材。One of the best machine learning books.

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林轩田 机器学习基石 配套教材 Learning From Data 下载
《Learning From Data》读书笔记——引言
“线性回归模型”原理小结
英文原版-Data Science from Scratch First Principles with Python 1st Edition

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by imple

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Linear Algebra and learning from Data(ppt,不是原书籍)

学习机器学习人工智能相关的线性代数和有关数据处理的相关知识(与原书籍无关,PPT)(Multiplication Ax and AB Column space of A Independent rows and basis Row rank = column rank )

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Basics of Linear Algebra for Machine Learning

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 letter

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MIT大牛Gilbert Strang新书:《线性代数与从数据中学习》抢先看
NSLayoutManager learning
数据仓库数据模型之:极限存储--历史拉链表
【阅读笔记】《Matterport3D: Learning from RGB-D Data inIndoor Environments》
求解Ax=0:主变量,特解-线性代数课时7(MIT Linear Algebra , Gilbert Strang)
[DEEP LEARNING An MIT Press book in preparation]Linear algebra
Machine Learning 1 Linear Algebra Review quiz

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Learning from data

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