Mathematics for Machine Learning

If you already have a background with linear algebra, calculus, statistics, then this will be a nice refresher applied to the subject in question, machine learning. In that regard, it serves perfectly as a way to organize your study to get into AI/ML in a deeper way. Certainly deeper than from a purely user perspective.If you don't have a background with linear algebra, calculus, statistics, it'll still provide a well organized studies plan for you to dive deeper.It cannot, of course, be a textbook for these areas, it would take hundreds, thousands of pages to do so, and that's clearly not feasible.What it does is introduce you to some concepts, refresh them, or refer you to further studies where there is a need to dive deeper in certain topics.The book is clearly organized, well illustrated. For that alone I'm thankful, for many mathematics textbooks, even the ones targeting the professional mathematician, make the fatal mistake of assuming the reader finds images insulting.They're not. Images help you organize thoughts visually, geometrically, providing important insights. For that alone, the content and organization, I would give the book 5 stars.The examples are well laid out, the cases well illustrated, giving room for the reader to breathe without being crushed by a dense monolith of rendered equations.Where it exceeds and stands above others is that the companion website provides, freely, the PDF of the book, an errata, instructor solutions to the exercises, and Jupyter Lab notebooks.While other publishers would try to rob the customer blind by offering each of these as a separate product, for a hefty sum naturally, this publisher thought it would best serve the reader to have access to all this content for free.Naturally, in this day and era, seeing someone focused on spreading knowledge for the sake of science and knowledge is commendable, and I cannot give me more than 5 stars sadly, for I would.If you read it this far, this is a no-brainer. Visit the website, take a look at the PDF, buy it, so that you can have the version with you for your daily studies, and the PDF for that morning reading on the tablet.The Jupyter notebooks make exploration fun and interesting, even if you're not experienced in the field.It does not assume you are an expert in these areas, though naturally, it would benefit you greatly if you have experience or if at least you have some textbooks on linear algebra and some knowledge of differential, integral calculus.To the authors, congratulations, and to the publisher, may you have a thousand years of prosperity and good fortune for making the auxiliary content freely available and in such a open and honest manner.Bravo.Highly recommended.

This book is excellent for brushing up your mathematics knowledge required for ML. It is very concise while still providing enough details to help readers determine important parts. This is my go-to if I need to review some concepts or brush up on my knowledge in general.I wouldn't recommend this book if you have absolutely no prior math experience though as it can be hard to digest and sometimes they would skip parts here and there in proofs and examples. Especially for the probability section, the concepts will be very hard to grasp without prior knowledge

Fits my needs pretty well. A well-curated collection of the essential math for AI and ML. I purchased the physical copy, despite having the free PDF, because I enjoyed it a lot and plan to re-read it with more detailed note-taking and highlighting on the book itself. I did have to consult other sources just to clear up some parts but was expected in reading a math book. Highly recommended!

Mathematics for Machine Learning provides an overview of much of the math needed to start working on machine learning problems. It is much more of an engineering math type book than a formal one and is thus a definition, example type book rather than theorem proof. It covers linear algebra, analytic geometry, matrix decomposition, vector calculus, probability and statistics as well as optimization techniques. It ends with the application of these ideas to machine learning for which the material goes from easy to follow to much more advanced. Thus the preliminary material is basic but the specific material requires more effort.The book is split into 2 parts, the first part being the mathematical preliminaries one is expected to know for machine learning applications. As mentioned above its largely linear algebra, probability and then convex analysis and optimization within multi-dimensional domains. As these subjects are all university courses in themselves, the coverage is rather light but sufficient for understanding how the ideas can be applied within data science. The book should really have focused more on the matrix decomposition chapter as the ideas in here are far more consequential than the sections on analytic geometry. The sections on vector calculus focus on backpropagation and ideas needed to understand adjoints and the probability sections provide material on the primary distributions encountered in most applications.The 2nd part is a step into actual problems faced and techniques required for handling data and model selection given parameterizing a model becomes the problem one really deals with rather than how to optimize a model once given a model choice. Linear regression is first tackled which is a major workhouse obviously, PCA is then discussed and then the author gets into more challenging domains like density estimation and classification though SVM techniques are used to highlight these topics rather than neural nets.I think its unlikely that one can really learn the math for data science from this book though it can be a quick refresher for those who need such. The pdf is available freely which is a very nice courtesy. There are a number of books which tackle the mathematical techniques one needs to know to engage in modern data science and this is one the easier side.

I had been looking for a book to bridge the gap between implementing machine learning code on the granular level and understanding it from a theoretical perspective and the search wasn't going well. Lots of other books that I tried before finding this one promised to help programmers become better mathematicians (or at least show them the math they need to learn in order to achieve that goal) but would almost always just provide code without context, or run through some incredibly basic, introductory level math without explaining at all how it connects to the various machine learning algorithms you'll be implementing as a programmer.This book, however, takes the math seriously, and is incredibly direct and efficient in the introduction of new, relevant topics in calculus, linear algebra, and probability and statistics that you'll need to know if you want to truly understand the libraries you're using. I find myself reading a section in the book, going back to a "dedicated" textbook on the subject at hand - linear algebra or calculus or probability and statistics - and further studying the material, and then going back to Mathematics for Machine Learning to make sure I understand the topic better. This is the exact learning flow that I wanted, and the book delivers. Can't recommend enough!

Even though I can get a free e-copy, I still like the paperback version because I flip through it occasionally. This book sketches a clear big picture of the knowledge tree for ML and provides necessary build blocks to help you build solid foundations in preparation for practical ML.You have to be aware this paperback version doesn't come with solutions. One of my reason to buy this is for the solutions. It turned out that only instructors can request solutions from the press company.

Just an unbelievable book. It may be a bit difficult to follow but complemented by a couple of online resources for when you're stuck it's awesome. Definitions are precise. Explanations are succinct.It is not intended to be, but is a masterpiece that brings out the beauty of mathematics.

Great book who wants to understand the maths behind the ML models, but some parts are rocket science :). I would definitely recommend this book to people in academia or to whom who has enough time to dive deep into theoretical aspect of the ML.

Nachdem ich vor 25 Jahren Informatik studiert habe und dort bereits "Neuronale Netze" (feed-forward back-propagation) kennengelernt hatte, wollte ich, motiviert durch den Hype der aktuellen AI (insbesondere machine learning sowie deep learning) mehr darüber lesen.Daher zunächst das "Standardwerk" (Titel "Deep Learning") gekauft. Die dort enthaltene Mathematik ist, meines Erachtens, so stark ver-klausuliert und auch von der Notation her schwer zu lesen, dass ich dieses Buch hier "Mathematics for Machine Learning" gekauft habe: Ich muss sagen/schreiben: Das ist die BESTE Darstellung der verschiedenen mathematischen Themenbereiche (Vektoren, Matrizen, Lineare Algebra, Wahrscheinlichkeitsrechnung, u.s.w.), die ich als Praktiker der Informatik je gesehen habe.Sehr gut verständlich (mit dem math. Grundwissen eines Informatikers), sehr tolle praxis-bezogene Beispiele zu den mathematischen Verfahren.Darüber hinaus in einem hervorragenden Englisch geschrieben, das wirklich Freude macht, es zu lesen.Ich denke, dass jeder, der sich intensiv mit Machine Learning auseinandersetzen möchte, hier sowohl ein Lehrwerk als auch ein Nachschlagewerk erhält.Übungen mit Lösungen (auf github) runden dieses Buch ab. Ich bin begeistert!!!

Buen libro !

The book is divided into two parts. Part-1 focuses on mathematical concepts and part-2 discusses some of the most common methods used in ML. It is must read book for beginners in ML.

Le basi matematiche di questo libro non sono da super specialisti. Ma anche per chi è un ricercatore, questo libro offre un approccio diverso su molti temi standard, facendoti guardare a cose che conosci bene da un punto di vista inaspettato

Les bases mathématiques et analyse numériques de niveau Master1 (Bac+4).Agréable à avoir en format papier.+ accès au site web pour suivre les quelques coquilles.Simple regret : impossible d'accéder aux corrections des nombreux exercices sans être un enseignant dans une faculté.