Mathematics For Machine Learning Deisenroth Pdf

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· 73 ratings · 17 reviews

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Dec 09, 2020 Mohamed rated it it was amazing

Remarkable!

I don't treat this one as an introductory book, but rather a "refresher" on the mathematics required for machine learning. Most of the book was a delight to read, I liked the slow building up of ideas in first chapters such as vector spaces, linear independence, basis, rank, linear mapping, inner products, orthogonality, .., etc. to the point that you become comfortable with them when used in more complex chapters, even if you had little or no background on such topics.

There were so m

Remarkable!

There were so many "Wow!"(s) moments, like geometrical interpretation of singular value decomposition, treating correlation as an inner product between two random variables, principal components as the eigenvalues and eigenvectors of data covariance matrix, latent variable perspective and many more..

Not to mention the amazing formatting, beautiful LaTeX and gorgeous figures that made me envious sometimes, for example:

Yet, some parts were not easy to grasp (or I completely skipped, like dual Lagrange optimization), and the Probability and Distributions chapter will be very hard to read without prior "wink ;)" knowledge on probability.

In general I really liked it and very much recommend it!

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I'm so intimidated by the enormous amount of terms and symbols, and couldn't say it's a beginner friendly book. So i'm gonna let go of this and try other books I'm so intimidated by the enormous amount of terms and symbols, and couldn't say it's a beginner friendly book. So i'm gonna let go of this and try other books ...more

Brilliant and Precise

The book is the missing piece between books like Artificial Intelligence: A Modern Approach and mathematics. It is recommended that you've had exposure to the mathematical topics prior to reading the book, but let that stop you if you're a beginner. Having said that, a course on single variable calculus ought to be under your belt.

Brilliant and Precise

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Jun 01, 2021 Cristián S rated it it was amazing

I loved this book, and how it is written. I have strong mathematical background, so the initial chapters were rather unnecessary, although I read them and I really enjoyed re-learning some mathematical details with some intuition I had lost. Then, the machine learning examples, including regression and Bayesian perspectives, is really good and complete, and it is really helpful to have a strong basis on the mathematics behind most popular models. Usually people that use these models don't stop t I loved this book, and how it is written. I have strong mathematical background, so the initial chapters were rather unnecessary, although I read them and I really enjoyed re-learning some mathematical details with some intuition I had lost. Then, the machine learning examples, including regression and Bayesian perspectives, is really good and complete, and it is really helpful to have a strong basis on the mathematics behind most popular models. Usually people that use these models don't stop to think on these details, but once you get them, you have a better perspective of what the models are doing. Also, pictures and examples are very nice.

In summary, it is a good book, gives strong mathematical and Bayesian perspective, good basis to explain how models work to others, but the initial chapters are too hard for someone without mathematical background, and at the same time, they are sometimes dull for someone that already knows them. Specially because it hops from very basic stuff in linear algebra, to complex optimization results. They are definitely not for someone that do not know them previously.

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If there is a book to start your machine learning journey the right way, whether you are a mathematics or computer science student, this would be it.

A piece of advice: I would not recommend reading this book if you did not have exposure to calculus, introductory linear algebra and probability theory. You should view the first part of the book as a quick refresher, and not as an introduction to these subjects.

Very difficult mathematics book. However, I feel like I came out the other side with some new mathemetical skills in my toolbox and a better understanding of the theory behind many machine learning algrorithms. However, this book is definitely a very tough read.

Perfect and free, what else do you want?

May 30, 2021 Giang Le is currently reading it

I'm half way through this book. It gives a very nice bird-eye overview of the main mathematical pillars often used in machine learning. Each chapter moves very fast and serves more as a dense summary of the main concepts rather than an introductory read. Overall, it's a very helpful text for someone looking for an overview of what background knowledge is needed to understand the machine learning literature or a review for those who have had prior exposure to these topics. I'm half way through this book. It gives a very nice bird-eye overview of the main mathematical pillars often used in machine learning. Each chapter moves very fast and serves more as a dense summary of the main concepts rather than an introductory read. Overall, it's a very helpful text for someone looking for an overview of what background knowledge is needed to understand the machine learning literature or a review for those who have had prior exposure to these topics. ...more

Apr 24, 2021 Zach rated it liked it

I've been working as an engineer with data scientists for several years. Aurelien Geron's book is essential if you're new to the field. I picked this up to get a better understanding of the math behind some ML approaches. It does a good job connecting the dots between the various areas of math and ML problems. I've been working as an engineer with data scientists for several years. Aurelien Geron's book is essential if you're new to the field. I picked this up to get a better understanding of the math behind some ML approaches. It does a good job connecting the dots between the various areas of math and ML problems. ...more

I really like this. Highly recommended.

This book also sparks my interest in Applied Mathematics after reading continuous optimization and probabilistic modelling chapters.

Some notes:
+) Recommend using pdf version of book from author github repository to have latest corrections/updates
+) Many figures can be rearranged to be closer to theirs context paragraph.

3.5/5 stars, rounded up. The earlier parts on the core concepts of mathematics for ML are better seen as a refresher than as a stand-alone introduction. The latter chapters, however, make good use of the toolset introduced in Chapters 1-7 by showing how one can arrive at classical machine learning models, thus understanding their properties and shortcomings.

Jul 07, 2021 Michiel rated it really liked it

Ideal as a reference for machine learning practitioners. Basic familiarity with the topics is recommended (otherwise, it is pretty dense), though a good book to identify gaps in one's knowledge. Also useful for general scientists who want to reorient into mathematical modelling. Ideal as a reference for machine learning practitioners. Basic familiarity with the topics is recommended (otherwise, it is pretty dense), though a good book to identify gaps in one's knowledge. Also useful for general scientists who want to reorient into mathematical modelling. ...more

An incredibly useful collection of—not just methods—but explanations, context and motivation for modern machine learning, accessible to advanced undergraduate and beginning graduate level.

A great crash course to brush up the math required for ML!

Mar 12, 2020 Robin Dong rated it really liked it

Just give this book a preliminary view.
It's easy to understand for most parts hence good for software developers (such as me) Just give this book a preliminary view.
It's easy to understand for most parts hence good for software developers (such as me) ...more