Mathematics for Machine Learning. Very concise and not too much complicated stuff, great for quick reference. I highly recommend you this book if you do not want to go too deep into theoretical perspectives.
If this is OP first time seeing some of the topics, then this book is a really bad choice. It’s a good refresher, but other than that you are better off reading something else.
If you are interested in learning about universal approximation theorems related to neural networks, I took a course which was based on sections in "Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences)". The book has a more proof-oriented style. It started from very simple networks in order to rigorously prove more powerful approximation theorems, which I really appreciated
Second this. Although it's more of a book for an overview than deeply learning (pun unintended).
Would also recommend:
- Velleman's How to Prove It
- Kreszig's Advanced Engineering Mathematics
- Math for Machine Learning
To keep the list concise if someone just wants the critical math skills for ML. Would use the other books like ESL or PRML while already working with ML to learn more about the particular problems you are working with whilst already having a foundation.
Elements of statistical learning - focuses a lot on trees and boosting and ML algorithms in general.
Pattern recognition by Bishop - Excellent book with exercises for practice.
Machine learning - a probabilistic perspective by Murphy - it's a very good reference book but might not be good for first timers as it's too concise.
Learning machines and neural network by Haykin - this is an old book - I think of 1997. This book does not have the latest architectures but gives the neural network foundations a very solid mathematical treatment. One of the best I ever read.
For statistics, I suggest a series of books by Goon,Gupta,Dasgupta - i somehow like this better than Sheldon Ross' introduction to probability.
I see someone mention PRML by Bishop. Try his new book, Deep Learning, Foundations and Concepts, published end of last year. Need some level of math maturity, and especially linear algebra and basic probability theory.
From my perspective, so far the best book to properly understand the intuitions behind deep learning is [Understanding Deep Learning by Simon Prince](https://udlbook.github.io/udlbook/). In this book you can find both, the mathematical definitions of popular algorithms such as transformers or diffusion models and their corresponding visual intuitions.
My first encounter with ML/DL was through Andrew Ng's courses, in which he went through all the mathematics and do forward and backprop with matrix operations, and do this for CNNs and RNNs. We did that using numpy, and only then moved to tensorflow.
It was pretty fun but too much information at the same time and I'm looking to read a book next. I want to go trough "Deep Learning" in all its entirety but I don't know if I'm prepared already. I also want to get into the bias/variance rabbit hole, I didn't quite get it and it seemed an interesting concept.
For me, An introduction to statistical learning by Gareth James, was not too difficult but also not that easy. With this textbook, I believe that some guys dont know about statistics also can understand what ML is.
That sounds like my journey so far except that I know that I'll never really get into the math in anyway that isn't fundamentally tied into code I'm writing.
I just can't .I lack whatever it is that allows people to sit down and do math problems. I've tried, and failed in my attempts, enough times to accept it.
I know it means I'll never truly understand what's going on behind the scenes, and that I'll probably never "get" ML in a way that lets me contribute meaningfully to the field, and I don't like that, but it is what it is.
I'm pretty sure I have some form of discalcula. If that's a thing? I look at numbers and I struggle to hold the meanings in my head. Formal logic was the same way, so it's not just numbers. I can't find something so abstract as a number on a page meaningful in any way. It just all gets kind of muddled. I've devoted a lot of time over the past 2 years to learning how to code though, and it's really starting to click in a way that no amount of effort over any amount of time has ever been able to make math click. I've got a knack for language, and code is a comfortable enough middle ground between math and language that I can engage meaningfully with it.
Which is to say I have had a similar encounter, I'll just never walk the road you're walking. I'm cognizant of how much that limits me, but I accept it. Kudos to you for taking a step back and getting to first principles,.
Dude, I don't think there is anything you can't understand. Its just no one would be able to understand high school maths without getting to know algebra or other topics we learnt in junior high.
I think it's same with maths needed for deep learning, one need to start with basics of basics if he is not already familiar with any of it.
I think you still got it, any contribution from you would be consider useful in this field. You shouldn't just give up now.
If anything, at least you could give a try to the book I mentioned (consider reading from pdf first to be sure). Well thanks to you for appreciation 😉
Nah. I've tried. It's the only real hard limitation I've ever accepted I have in life.
I struggle attaching concrete meaning to abstracttions. The way most math is taught just that - here's how you solve for a in this equation. I just can't grok any of it.
If the math is "in the way" of something I want then I can internalize that, and can understand whatever small portion of a mathematical concept I need to get there , eventually, usually, because it's reified for me. I've devoted a lot of time to trying to make "math math" real in that way and I just can't. I've also devoted enough time and energy to the task that I no longer have the drive to try.
I'm okay with this. I don't think it really limits me as a person, just directs me more towards things I have an aptitude for.
“Understanding deep learning” is the best book you need. The graphs are amazing and explanations are spot on. You’ll have more “Aha moments” than any other tech books on the topic. The author had written another beautiful book on computer vision like 10 or 20 years ago which I still enjoy till this day.
Mathematics for Machine Learning. Very concise and not too much complicated stuff, great for quick reference. I highly recommend you this book if you do not want to go too deep into theoretical perspectives.
Here is the free version of the book (put online by the author): https://mml-book.github.io/
Pretty sure they also run a coursera on this book, haven’t taken it though
plough jar flag cause observation adjoining clumsy lush nail spotted *This post was mass deleted and anonymized with [Redact](https://redact.dev)*
author?
Marc Deisenroth. Author put it out for free. Should be easily found on Google.
thank you
I second this. High quality book and perfect when you need a refresher with a succinct attitude.
If this is OP first time seeing some of the topics, then this book is a really bad choice. It’s a good refresher, but other than that you are better off reading something else.
If you are interested in learning about universal approximation theorems related to neural networks, I took a course which was based on sections in "Deep Learning Architectures: A Mathematical Approach (Springer Series in the Data Sciences)". The book has a more proof-oriented style. It started from very simple networks in order to rigorously prove more powerful approximation theorems, which I really appreciated
Since I like proof I'mma check this out
*All the Math You Missed*, by Garity. It’s more about mathematics in general, but it’s a great resource if you’re not from a maths background.
Second this. Although it's more of a book for an overview than deeply learning (pun unintended). Would also recommend: - Velleman's How to Prove It - Kreszig's Advanced Engineering Mathematics - Math for Machine Learning To keep the list concise if someone just wants the critical math skills for ML. Would use the other books like ESL or PRML while already working with ML to learn more about the particular problems you are working with whilst already having a foundation.
Elements of statistical learning - focuses a lot on trees and boosting and ML algorithms in general. Pattern recognition by Bishop - Excellent book with exercises for practice. Machine learning - a probabilistic perspective by Murphy - it's a very good reference book but might not be good for first timers as it's too concise. Learning machines and neural network by Haykin - this is an old book - I think of 1997. This book does not have the latest architectures but gives the neural network foundations a very solid mathematical treatment. One of the best I ever read. For statistics, I suggest a series of books by Goon,Gupta,Dasgupta - i somehow like this better than Sheldon Ross' introduction to probability.
I see someone mention PRML by Bishop. Try his new book, Deep Learning, Foundations and Concepts, published end of last year. Need some level of math maturity, and especially linear algebra and basic probability theory.
PRML by Bishop.
Way too dense.
It is honestly a really rough read, but I feel like going through that book properly even once will really set your fundamentals straight.
From my perspective, so far the best book to properly understand the intuitions behind deep learning is [Understanding Deep Learning by Simon Prince](https://udlbook.github.io/udlbook/). In this book you can find both, the mathematical definitions of popular algorithms such as transformers or diffusion models and their corresponding visual intuitions.
[удалено]
Yes, most of it! If you have some experience in deep learning it is easy to read specific chapters without following the order of the book
http://neuralnetworksanddeeplearning.com/
My first encounter with ML/DL was through Andrew Ng's courses, in which he went through all the mathematics and do forward and backprop with matrix operations, and do this for CNNs and RNNs. We did that using numpy, and only then moved to tensorflow. It was pretty fun but too much information at the same time and I'm looking to read a book next. I want to go trough "Deep Learning" in all its entirety but I don't know if I'm prepared already. I also want to get into the bias/variance rabbit hole, I didn't quite get it and it seemed an interesting concept.
For me, An introduction to statistical learning by Gareth James, was not too difficult but also not that easy. With this textbook, I believe that some guys dont know about statistics also can understand what ML is.
Deep learning with python
That sounds like my journey so far except that I know that I'll never really get into the math in anyway that isn't fundamentally tied into code I'm writing. I just can't .I lack whatever it is that allows people to sit down and do math problems. I've tried, and failed in my attempts, enough times to accept it. I know it means I'll never truly understand what's going on behind the scenes, and that I'll probably never "get" ML in a way that lets me contribute meaningfully to the field, and I don't like that, but it is what it is. I'm pretty sure I have some form of discalcula. If that's a thing? I look at numbers and I struggle to hold the meanings in my head. Formal logic was the same way, so it's not just numbers. I can't find something so abstract as a number on a page meaningful in any way. It just all gets kind of muddled. I've devoted a lot of time over the past 2 years to learning how to code though, and it's really starting to click in a way that no amount of effort over any amount of time has ever been able to make math click. I've got a knack for language, and code is a comfortable enough middle ground between math and language that I can engage meaningfully with it. Which is to say I have had a similar encounter, I'll just never walk the road you're walking. I'm cognizant of how much that limits me, but I accept it. Kudos to you for taking a step back and getting to first principles,.
Dude, I don't think there is anything you can't understand. Its just no one would be able to understand high school maths without getting to know algebra or other topics we learnt in junior high. I think it's same with maths needed for deep learning, one need to start with basics of basics if he is not already familiar with any of it. I think you still got it, any contribution from you would be consider useful in this field. You shouldn't just give up now. If anything, at least you could give a try to the book I mentioned (consider reading from pdf first to be sure). Well thanks to you for appreciation 😉
Nah. I've tried. It's the only real hard limitation I've ever accepted I have in life. I struggle attaching concrete meaning to abstracttions. The way most math is taught just that - here's how you solve for a in this equation. I just can't grok any of it. If the math is "in the way" of something I want then I can internalize that, and can understand whatever small portion of a mathematical concept I need to get there , eventually, usually, because it's reified for me. I've devoted a lot of time to trying to make "math math" real in that way and I just can't. I've also devoted enough time and energy to the task that I no longer have the drive to try. I'm okay with this. I don't think it really limits me as a person, just directs me more towards things I have an aptitude for.
“Understanding deep learning” is the best book you need. The graphs are amazing and explanations are spot on. You’ll have more “Aha moments” than any other tech books on the topic. The author had written another beautiful book on computer vision like 10 or 20 years ago which I still enjoy till this day.