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Télécharger Linear Algebra and Learning from Data Livre PDF Gratuit

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2019-01-31
Linear Algebra and Learning from Data - de Gilbert Strang (Author)

Details Linear Algebra and Learning from Data

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Le Titre Du FichierLinear Algebra and Learning from Data
Date de Lancement2019-01-31
TraducteurGiuliano Frimet
Quantité de Pages158 Pages
La taille du fichier58.81 MB
LangueAnglais et Français
ÉditeurArthéna
ISBN-103436207495-OAQ
Format de FichierEPub AMZ PDF MBP RPT
de (Auteur)Gilbert Strang
Digital ISBN032-6011835933-KTI
Nom de FichierLinear-Algebra-and-Learning-from-Data.pdf

Télécharger Linear Algebra and Learning from Data Livre PDF Gratuit

Cette prise d’écran est extraite du livre Deep Learning de I Goodfellow Y Bengio et A Courville chapitre 2 Linear Algebra sur l’ACP Trois pages de charabia Forcément ça paraît moins cool

The concepts of Linear Algebra are crucial for understanding the theory behind Machine Learning especially for Deep Learning They give you better intuition for how algorithms really work under the hood which enables you to make better decisions

Linear algebra is the branch of mathematics… This is the 3rd post of blog post series ‘Data Science The Complete Reference’ this post covers these topics related to data science…

In the first course on Linear Algebra we look at what linear algebra is and how it relates to data Then we look through what vectors and matrices are and how to work with them The second course Multivariate Calculus builds on this to look at how to optimize fitting functions to get good fits to data It starts from introductory calculus and then uses the matrices and vectors from the first

Learn the concepts and methods of linear algebra and how to use them to think about computational problems arising in computer science Coursework includes building on the concepts to write small programs and run them on real data

Learn here interactive data visualization using Plotly and Cufflinks which is more better than traditional matplotlib and seaborn static plots Please watch it and do not forget to like Please watch it and do not forget to like

Linear systems Direct sparse factorizations Krylov subspace methods and preconditioners Matrix functions Theory and algorithms Keywords linear systems eigenvalue problems matrix functions Learning Prerequisites Required courses Linear Algebra Numerical Analysis Learning Outcomes By the end of the course the student must be able to Choose method for solving a specific problem


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