Practical Linear Algebra For Data Science

Author : Mike X Cohen
Publisher : "O'Reilly Media, Inc."
Page : 329 pages
File Size : 55,7 Mb
Release : 2022-09-06
Category : Computers
ISBN : 9781098120580

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Practical Linear Algebra for Data Science by Mike X Cohen Pdf

If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms. Ideal for practitioners and students using computer technology and algorithms, this book introduces you to: The interpretations and applications of vectors and matrices Matrix arithmetic (various multiplications and transformations) Independence, rank, and inverses Important decompositions used in applied linear algebra (including LU and QR) Eigendecomposition and singular value decomposition Applications including least-squares model fitting and principal components analysis

Author : MIKE. COHEN
Publisher : O'Reilly Media
Page : 300 pages
File Size : 50,7 Mb
Release : 2022-11-30
Category : Computers
ISBN : 1098120612

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Practical Linear Algebra for Data Science by MIKE. COHEN Pdf

If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms. Ideal for practitioners and students using computer technology and algorithms, this book introduces you to: The interpretations and applications of vectors and matrices Matrix arithmetic (various multiplications and transformations) Independence, rank, and inverses Important decompositions used in applied linear algebra (including LU and QR) Eigendecomposition and singular value decomposition Applications including least-squares model fitting and principal components analysis

Author : Mike Cohen
Publisher : Unknown
Page : 0 pages
File Size : 49,9 Mb
Release : 2022
Category : Electronic
ISBN : OCLC:1295832509

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Practical Linear Algebra for Data Science by Mike Cohen Pdf

If you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications. This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms. Ideal for practitioners and students using computer technology and algorithms, this book introduces you to: The interpretations and applications of vectors and matrices Matrix arithmetic (various multiplications and transformations) Independence, rank, and inverses Important decompositions used in applied linear algebra (including LU and QR) Eigendecomposition and singular value decomposition Applications including least-squares model fitting and principal components analysis.

Author : Jeffrey A. Fessler,Raj Rao Nadakuditi
Publisher : Cambridge University Press
Page : 453 pages
File Size : 50,5 Mb
Release : 2024-04-30
Category : Computers
ISBN : 9781009418140

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Linear Algebra for Data Science, Machine Learning, and Signal Processing by Jeffrey A. Fessler,Raj Rao Nadakuditi Pdf

Master matrix methods via engaging data-driven applications, aided by classroom-tested quizzes, homework exercises and online Julia demos.

Author : Mike X. Cohen
Publisher : Unknown
Page : 0 pages
File Size : 44,9 Mb
Release : 2023
Category : Algebras, Linear
ISBN : 757660588X

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Practical Linear Algebra for Data Science by Mike X. Cohen Pdf

Author : Peter Bruce,Andrew Bruce
Publisher : "O'Reilly Media, Inc."
Page : 395 pages
File Size : 41,6 Mb
Release : 2017-05-10
Category : Computers
ISBN : 9781491952917

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Practical Statistics for Data Scientists by Peter Bruce,Andrew Bruce Pdf

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Author : Thomas Nield
Publisher : "O'Reilly Media, Inc."
Page : 350 pages
File Size : 54,6 Mb
Release : 2022-05-26
Category : Computers
ISBN : 9781098102906

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Essential Math for Data Science by Thomas Nield Pdf

Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market

Author : Amirsina Torfi
Publisher : Unknown
Page : 64 pages
File Size : 40,7 Mb
Release : 2019-12-26
Category : Electronic
ISBN : 1651122636

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Practical Linear Algebra for Machine Learning by Amirsina Torfi Pdf

Machine Learning is everywhere these days and a lot of fellows desire to learn it and even master it! This burning desire creates a sense of impatience. We are looking for shortcuts and willing to ONLY jump to the main concept. If you do a simple search on the web, you see thousands of people asking "How can I learn Machine Learning?", "What is the fastest approach to learn Machine Learning?", and "What are the best resources to start Machine Learning?" \textit. Mastering a branch of science is NOT just a feel-good exercise. It has its own requirements.One of the most critical requirements for Machine Learning is Linear Algebra. Basically, the majority of Machine Learning is working with data and optimization. How can you want to learn those without Linear Algebra? How would you process and represent data without vectors and matrices? On the other hand, Linear Algebra is a branch of mathematics after all. A lot of people trying to avoid mathematics or have the temptation to "just learn as necessary." I agree with the second approach, though. \textit: You cannot escape Linear Algebra if you want to learn Machine Learning and Deep Learning. There is NO shortcut.The good news is there are numerous resources out there. In fact, the availability of numerous resources made me ponder whether writing this book was necessary? I have been blogging about Machine Learning for a while and after searching and searching I realized there is a deficiency of an organized book which \textbf teaches the most used Linear Algebra concepts in Machine Learning, \textbf provides practical notions using everyday used programming languages such as Python, and \textbf be concise and NOT unnecessarily lengthy.In this book, you get all of what you need to learn about Linear Algebra that you need to master Machine Learning and Deep Learning.

Author : Charu C. Aggarwal
Publisher : Springer Nature
Page : 507 pages
File Size : 48,7 Mb
Release : 2020-05-13
Category : Computers
ISBN : 9783030403447

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Linear Algebra and Optimization for Machine Learning by Charu C. Aggarwal Pdf

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.

Author : Philip N. Klein
Publisher : Unknown
Page : 530 pages
File Size : 50,8 Mb
Release : 2013-07
Category : Algebras, Linear
ISBN : 061585673X

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Coding the Matrix by Philip N. Klein Pdf

An engaging introduction to vectors and matrices and the algorithms that operate on them, intended for the student who knows how to program. Mathematical concepts and computational problems are motivated by applications in computer science. The reader learns by "doing," writing programs to implement the mathematical concepts and using them to carry out tasks and explore the applications. Examples include: error-correcting codes, transformations in graphics, face detection, encryption and secret-sharing, integer factoring, removing perspective from an image, PageRank (Google's ranking algorithm), and cancer detection from cell features. A companion web site, codingthematrix.com provides data and support code. Most of the assignments can be auto-graded online. Over two hundred illustrations, including a selection of relevant "xkcd" comics. Chapters: "The Function," "The Field," "The Vector," "The Vector Space," "The Matrix," "The Basis," "Dimension," "Gaussian Elimination," "The Inner Product," "Special Bases," "The Singular Value Decomposition," "The Eigenvector," "The Linear Program" A new edition of this text, incorporating corrections and an expanded index, has been issued as of September 4, 2013, and will soon be available on Amazon.

Author : Gilbert Strang
Publisher : Wellesley-Cambridge Press
Page : 446 pages
File Size : 45,6 Mb
Release : 2019-01-31
Category : Computers
ISBN : 0692196382

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Linear Algebra and Learning from Data by Gilbert Strang Pdf

Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.

Author : Stephen Boyd,Lieven Vandenberghe
Publisher : Cambridge University Press
Page : 477 pages
File Size : 45,5 Mb
Release : 2018-06-07
Category : Business & Economics
ISBN : 9781316518960

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Introduction to Applied Linear Algebra by Stephen Boyd,Lieven Vandenberghe Pdf

A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.

Author : Moshe Haviv
Publisher : World Scientific Publishing Company
Page : 0 pages
File Size : 41,8 Mb
Release : 2023-06-28
Category : Electronic
ISBN : 9811276226

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Linear Algebra for Data Science by Moshe Haviv Pdf

This book serves as an introduction to linear algebra for undergraduate students in data science, statistics, computer science, economics, and engineering. The book presents all the essentials in rigorous (proof-based) manner, describes the intuition behind the results, while discussing some applications to data science along the way. The book comes with two parts, one on vectors, the other on matrices. The former consists of four chapters: vector algebra, linear independence and linear subspaces, orthonormal bases and the Gram-Schmidt process, linear functions. The latter comes with eight chapters: matrices and matrix operations, invertible matrices and matrix inversion, projections and regression, determinants, eigensystems and diagonalizability, symmetric matrices, singular value decomposition, and stochastic matrices. The book ends with the solution of exercises which appear throughout its twelve chapters.

Author : Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Publisher : Cambridge University Press
Page : 391 pages
File Size : 48,9 Mb
Release : 2020-04-23
Category : Computers
ISBN : 9781108470049

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Mathematics for Machine Learning by Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong Pdf

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Author : Ramon Base
Publisher : Unknown
Page : 173 pages
File Size : 51,9 Mb
Release : 2019-07-26
Category : Electronic
ISBN : 1083050729

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Data Science From Scratch by Ramon Base Pdf

Buy the Paperback version of this book, and get the Kindle eBook version included for FREE Do you want to know all about data science? Do you really want to understand why it is the future in all the most demanding job? The truth is: Data science is the need of the hur. Over the last few years, it has changed the concept of technology. It is adding value to every model of business by using deep learning and statistics. Technological benefits of data science cannot also be ignored. The demand for data science is increasing rapidly. The basic reason behind this is the massive boom in the data generated and retained by the companies. Also known as "big data", data scientists make the best use of this available information and figure out their best use. The team of data scientists also help in generating a good amount of analytics from the information available. This information brings clarity to people on how to interact with the web and are the foundations on which most of the critical business strategies rest. The demand for the data scientists is gradually increasing and so there must be proper learning resources for them to cultivate their knowledge. DOWNLOAD: Data Science from Scratch, Ultimate Guide to Master Data Mining and data-Analytic from Linear Algebra and Statistics to Practical Examples of Neural Networks and Machine Learning in Python. A data scientist has the following tasks to do: Analyse data Modelling/statistics Engineering/prototyping A lot of data is available in every organization. All of them are not that easy to format. A data scientist's job mainly revolves around formatting these data, keeping in mind some set of rules. The goal of this book is simple: The book provides a perfect insight into data science from the very scratch. It clearly explains the difference between data science and big data, explaining the concept of big data very concisely and clearly. Operations on data are also very clearly explained in this eBook. The book also stresses on Ultimate Guide to Master Data Mining and data-Analytic from Linear Algebra and Statistics to Practical Examples of Neural Networks and Machine Learning in Python. You will also learn: What is big data Difference between Data Science and big data Operations on data munging, cleaning, manipulating and rescaling data basic linear algebra with exercises Statistics and basic concept Decision tree and how to use them Languages required for data science Basics of python Neural network and what to use for (with examples) Machine learning (with examples) Importance of data science in job and business Who is a data scientist Data Science and its applications Would you like to know more? Download the eBook, Data Science from Scratch, and know about the trend that is ruling the world today. Scroll to the top of the page and select the buy now button.