Newtonian Press Coding the Matrix: Linear Algebra through Applications to Computer Sc Paperback – 3 September 2013

Substantial reference volume

Coniuga la teoria dell’algebra lineare senza ridondanza formale con la sua applicazione pratica in modo strutturato ed esemplificato in varie campi . Consigliato assolutamente insieme al testo del Prof.Strang del MIT: Algebra lineare e sue applicazioni (Linear Algebra and Its Applications) ... se poi si vuole chiudere la questione del tutto allora integratelo con Linear algebra done right di Sheldon AxlerVoto 9,5

Excelente libro, muy recomendable para practicar el fabuloso mundo del álgebra lineal con el uso de lenguaje de programación Python

Nach einer kurzen Einführung in die Programmiersprache Python bespricht der Autor die wesentlichen algebraischen Grundlagen: Körper und Vektor-/Lineare Räume (Kap. 2+3). Kap.4 handelt von den vielfältigen Anwendungen von Matrizen: Gleichungssysteme, Codierungs-Theorie und 2D-Transfomationen. Kap.5 beschäftigt sich mit dem Problem der Basis-Darstellung bzw. Änderung; hier wird auch auf die geometrische Behandlung der Perspektive eingegangen.Kap. 6 diskutiert Dimension und Rang von Linearen Räumen. Eine ausführliche Diskussion der Gauß-Elimination findet sich in Kap.7. Die beiden folgenden Kapitel 8+9 umfassen das innere Produkt und die Orthogonalisierung von Matrizen und ihre numerische Anwendung wie die QR-Faktorisierung. Kap.10 zeigt eine Einführung in die diskrete Fourier-Analyse und Anwendung von Wavelets. Kap.11 erklärt die Singulärwert (SVD)-Zerlegung.Kap.12 ist dem Eigenwert-Problem gewidmet; erwähnt wird hier die Anwendung von Markow-Ketten. Das Buch schließt mit dem Kap.13 über Lineare Programmierung. Insgesamt gesehen handelt es sich um eine anschauliche und ausführliche Darstellung (512 Seiten!) der Linearen Algebra; gut gewählt sind die zahlreichen Anwendungen. Empfohlen für alle mit englischer Sprachkenntnis (gilt nur für die Printausgabe des Buchs, für die sich eine Errata-Liste codingthematrix.com/Errata1.pdf im Internet findet)!Als Einschränkung ist zu sagen, dass die im Buch verwendeten Python-Anweisungen überholt sind. Die neueren Bibliotheken "numpy" und "sympy" stellen alle benötigten Datentypen wie "Matrix" oder "vector" und ihre Verknüpfungen bereit!

I rarely write book reviews but I am compelled to write one for Coding the Matrix. This book first caught my attention when a course by the same name was offered at Coursera. I did not enroll in the course but instead bought the book for self study at some stage. This year, I spent 5 months working through the problems in the book (I am down to the last 2 of the 14 chapters) and I just want to say that I really wish there was a book like this in bookstores 20 years ago. What a fantastic way to teach Linear Algebra!! Previously, I had tried working through Gilbert Strang's book and video lectures on Linear Algebra but the material never stuck in my head. This book is quite different in its approach because it spends a lot of time providing the intuition behind fundamental concepts. What is the intuition behind a Matrix? What is the "meaning" of Matrix multiplication? What really is a Vector Space? What is the relationship between a Matrix and a Function? The author goes about explaining these basic concepts using a combination of worked exercises and hands on Python implementations. After working through this book I am convinced that implementing Linear Algebra algorithms and applying them to real world problems is the most effective way to learn the subject.The hard copy book has several typos and errors but the Kindle version has been updated to fix most of these. Still, before you start, I suggest downloading the errata from the book's website just to be sure. The book has a short intro on Python which I thought was quite sufficient to tackle the programming exercises. This book requires real hard work if you want to get through it. Many times (especially in the Orthogonalization and Special Bases chapters) I found the going tough. But don't be discouraged - it is worth the effort. Now I really understand what QR factorization is about. SVD? No worries. The chapter on SVD starts with the absolute basics to explain how to derive the SVD formula and what the various component matrices really mean. I don't think I will ever see a A * A' multiplication in any other book without recollecting the beautiful explanation of orthonormal vectors from this book. The sad part for me is that I had to use all these concepts in the past working as a quant analyst at a bank, not knowing what these concepts really meant. So yeah, I wish it was published ages ago.Thank you Prof Klein for writing this book. The teaching technique you have employed for such a complicated topic is unique and effective. Looking forward to the next edition.