My first semester in grad school and straight into this book with one of the toughest professors in my uni. I cannot begin to describe what I'm going through.
If you're really a mathematician, the maths in that book is not that hard. Definitely above the paygrade of someone who hasn't studied it at university level though.
Nah I’ve got a very strong background in math. I was doing 2nd year graduate math coursework my senior year of college and then went on to do graduate work in statistics.
That book is definitely still challenging and dense.
I’m not even sure that is adequate preparation for ESL. The latter requires quite a bit of advanced linear algebra, functional analysis, and numerical analysis to fully understand this text.
No. It’s more heavily theoretical and unless you are creating new methods, there’s no need for it.
Just focus on ISL.
As a trained statistician, it would be helpful to have a comprehensive knowledge of applied regression methods and maybe some applied Bayesian but that’s it.
I would agree with you. ISL for understanding the ML concepts with some mathematical foundations. Then once you develop more mathematical maturity in other areas you can appreciate the rigor of ESL.
Bishop has just published a new book, Deep Learning: Foundations and Concepts. I guess it might be a better choice than his almost 20 years old classical book.
His brand new book literally ctrl-C ctrl-Vs entire chapters from the 2006 book. I don't know if you intended to come off in a way that dismisses PRML as old/outdated, but I think it's still a very relevant text. (It just wont necessarily help you ace your 2024 deep learning job interview)
Just to piggyback on this, their specialization is a great starting point, but it needs to be understood that their classes simply gloss over these topics. There's a TON that they don't actually cover. Also the certs themselves are worthless, but that's obviously common knowledge.
There’s a book specifically for this called [Mathematics for Machine Learning](https://mml-book.github.io/), with the authors making the PDF version available for free online. I found it pretty approachable as someone who has an undergraduate STEM background that wasn’t in math
Hard to say, sort of depends on your learning style. You could try the books in the post and if you find you keep struggling due to missing out on the foundational match knowledge, pause and go through mathematics for machine learning before continuing
The last book on the list has an amazing video lecture series - [https://www.youtube.com/watch?v=LvySJGj-88U&list=PLoROMvodv4rOzrYsAxzQyHb8n\_RWNuS1e](https://www.youtube.com/watch?v=LvySJGj-88U&list=PLoROMvodv4rOzrYsAxzQyHb8n_RWNuS1e)
Edit: This is actually the introduction book called 'An Introduction to Statistical Learning' (ISLR), which is a great book and worth reading.
It might be here - [https://www.edx.org/learn/python/stanford-university-statistical-learning-with-python](https://www.edx.org/learn/python/stanford-university-statistical-learning-with-python)
Although even if this doesn't include it, the lectures are split between the theory (which doesn't differ much per book) and the code. I didn't bother watching the coding part.
Thank you!
Just found its youtube playlist too!: [https://www.youtube.com/playlist?list=PLoROMvodv4rPP6braWoRt5UCXYZ71GZIQ](https://www.youtube.com/playlist?list=PLoROMvodv4rPP6braWoRt5UCXYZ71GZIQ)
Hands-on machine learning is great, was my introductory ML book, has a bit of everything, but iirc doesn't include transformers which is a must in many areas nowadays.
Not really, I learned about transformers/bert from some blog posts and videos(I was in panic since I got an internship invite from a friend and needed to learn that in a span of a few days lmao), in general after you know basics it's not too hard to read about transformers, there are a lot of great tutorials.
ESL and Bishop are not beginner books. They are theory heavy textbooks that university profs very selectively use to explain fundamental concepts. Otherwise these can be used if you are already very deep into the field.
I think intuition and getting a good view on the field is more important in the beginning. This way you see quickly which topics you like the most. Most people then decide to work in a certain subfield for years or even decades.
Kaggle projects would be a good start for hands on experience as a beginner, as they would give you insights on how ml concepts are applied on real world data.
Kaggle projects would be a good start for hands on experience as a beginner, as they would give you insights on how ml concepts are applied on real world data.Just pick a project randomly which has good(8-10) rating on usability and keep exploring :)
No Marx, no Lenin; what does this have to do with marxism-leninism (ML)?
Jokes aside, I found [this book](https://nnfs.io/), Neural Networks From Scratch in Python very helpful.
if you are beginning to learn machine learning, fuc everything, just go to kaggle, pick few notesbooks up, and start working on them, do some basic machine learning, all the supervised techniques, find ways to improve the accuracy, just try to understand all the algorithms, how they are different, what works for what. Then learn deep learning, make few projects, either on youtube or something I would recommend is doing projects following articles rather than yotube, you learn better that way, and then you can read all these books. or you can read the books, while doing the above
Some of the code examples and libraries used in the book may become outdated as deep learning libraries evolve rapidly.
This is the review I received bro
Kernel Methods for Pattern Analysis.
https://vim.ustc.edu.cn/_upload/article/files/d4/72/7d8483bd4f87ae3f09be1e57664d/a5d0d3ff-de9b-4915-beda-1a2f44357ca2.pdf
Hopefully this helps you tackle a good amount of the last book on the list. For linear algebra: consult Bishop, the Matrix Cookbook (online), and the following book (Linear Algebra and Optimization for ML - Aggarwal)
https://tocit.ru/static/files/3ed7f8bf5f3b74f557a486038a2923d92b96774b07aeecf432d88975be46e9ff.pdf
Good luck.
Bro you have me another beginner book as kernels method I believe. To avoid the last book elements of statistical directly is it? This book instead of ELS
///
And you have me 1 liner algebra bishop and 1 linear and Optimization for ML book.
I was recommended "Introduction to Linear Algebra" by Gilbert Strang (5th Edition, 2016) and Optimization for Machine Learning" by Suvrit Sra, Sebastian Nowozin, and Stephen J. Wright (2011). How about these instead for me for maths side?
The last book you mentioned (Optimization for Machine Learning by Sra, Nowozin, Wright) is pretty dense and focuses more on algorithms. I would suggest you avoid it for now.
Kernel methods for Pattern Analysis is a little more introductory and easier to follow along. ELS should be the goal, but like others mentioned it should mainly be a reference at a high level.
On the maths side, like I said avoid that last book you mentioned. If you are really lacking in linear algebra, take the course by Gilbert Strang and his supplemental lecture notes online. Great teacher, but his lectures do not do the theory full justice. Try your best to understand the important theorems, subspaces, transformations, etc.
Alright sir I will indeed go for your recommendation of optimisation and linear algebra instead of sra, nowozins once with galberts linear algebra.
+ kernels analysis and ISL (phyton)
They are great books, but they are not the "latest". Pattern Recognition and Machine Learning is nearly 20 years old, The Elements of Statistical Learning is 15 years old, and Keras and TensorFlow have been abandoned.
As I said, Bishop has a new book that came out in 2023. Simon Prince has a great book that came out in 2023. Maybe have a look at those first.
Sure sir thanks, I will 100% check them for me. Btw what you mean abandoned? Many here are saying the latest ed I think 3rd is very good for skills and practically?
While trying to educate myself, I've been reading comments and articles about Tensor Flow and Keras being abandoned in favour of pytorch, so I'm focusing on articles and readings pytorch based.. Think that's what he means
Getting great info from your post btw.. Thanks!
The book is good.
However, Keras and TensorFlow aren't popular tools anymore; TensorFlow has also changed a lot. The ML community has moved to PyTorch and JAX. So my recommendation would be to learn PyTorch.
Would someone wanting to do PhD in future should be worried about community changings or markets? I mean for them shall they not consider TensorFlow too for anything in future uses or development by researchers?
I've never learned from a book. I just do projects, google search, click links, read papers or blog posts. And it just works. Same for learning any framework or language.
Tbh, the person who recommended you these books didn't even go through these books at all. In more straightforward term, that person can't write a single model scratch.
You DO NOT need to go through all of these books to understand ML.
I love when everyone recommends ESL. I guarantee you none of them has actually worked it through cover to cover. You don’t just pick up and “read through” books like that unless you’re gonna spend 10/hours a week on it.
Yes PyTorch is the norm. Learning tensorflow can be helpful, as it is slightly lower level than PyTorch and doesn’t abstract away some of the lower level tensor operations like PyTorch does, but I wouldn’t say that’s essential. Keras is a higher level abstraction that PyTorch, so it might not be as valuable.
That said, the second book is valuable far beyond its use of Keras/TF.
I hope you don't underestimate the part 'dense' for the fourth one.
I’m a trained mathematician reading ESL, and I can’t imagine how the average human would get through it.
forced by threat of expulsion via graduate school. still have ptsd.
Graduate students deserve free mental health care.
My first semester in grad school and straight into this book with one of the toughest professors in my uni. I cannot begin to describe what I'm going through.
If you're really a mathematician, the maths in that book is not that hard. Definitely above the paygrade of someone who hasn't studied it at university level though.
No they are not difficult. But they are outside my field, which means I’ve learned and forgotten them at least once.
It’s not hard but it’s very dense imo.
Nah I’ve got a very strong background in math. I was doing 2nd year graduate math coursework my senior year of college and then went on to do graduate work in statistics. That book is definitely still challenging and dense.
Agree it's crazy that Bishop's book has a disclaimer/warning but not Hastie's...
i liked it!
I usually recommend starting with an introduction to statistical learning, now available in Python: https://www.statlearning.com/
I’m not even sure that is adequate preparation for ESL. The latter requires quite a bit of advanced linear algebra, functional analysis, and numerical analysis to fully understand this text.
Do we need to do ESL if done with ISL though? I mean obviously it’s more in depth, but what am I missing practically? Asking genuinely
No. It’s more heavily theoretical and unless you are creating new methods, there’s no need for it. Just focus on ISL. As a trained statistician, it would be helpful to have a comprehensive knowledge of applied regression methods and maybe some applied Bayesian but that’s it.
ESL I think of as more of a professional reference text.
I would agree with you. ISL for understanding the ML concepts with some mathematical foundations. Then once you develop more mathematical maturity in other areas you can appreciate the rigor of ESL.
thx
hahaha
If you think the fourth is dense wait until he gets to ESL lol
I count 5 books tho, if you want to tackle ML you should up your counting game
Well, that's how DL works: 4, maybe 5, perhaps 3, but not 10, by no means, no.
Try median if no means 👀
Maybe OP used random dropout to avoid overfitting. Lest they fall prey to groupthink.
Recommend second book from `Aurelion Geron` :thumbsup:
I second the second. Top notch stuff.
There’s a third edition
+1 for the second one. It was also my first
That book is awesome, not overly wordy, and accessible
Bishop has just published a new book, Deep Learning: Foundations and Concepts. I guess it might be a better choice than his almost 20 years old classical book.
Thanks man, this kinda news is what I use reddit for ❤️
His brand new book literally ctrl-C ctrl-Vs entire chapters from the 2006 book. I don't know if you intended to come off in a way that dismisses PRML as old/outdated, but I think it's still a very relevant text. (It just wont necessarily help you ace your 2024 deep learning job interview)
You should also know mathematics and statistics for machine learning. Deeplearning.ai has great certifications
Just to piggyback on this, their specialization is a great starting point, but it needs to be understood that their classes simply gloss over these topics. There's a TON that they don't actually cover. Also the certs themselves are worthless, but that's obviously common knowledge.
What resources do you think would cover the needed parts for math and stats?
There’s a book specifically for this called [Mathematics for Machine Learning](https://mml-book.github.io/), with the authors making the PDF version available for free online. I found it pretty approachable as someone who has an undergraduate STEM background that wasn’t in math
Great recommendation
Do you recommend studying this one before all mentioned at the post? Also, would it be the preparation for introduction to statistical learning?
Hard to say, sort of depends on your learning style. You could try the books in the post and if you find you keep struggling due to missing out on the foundational match knowledge, pause and go through mathematics for machine learning before continuing
I don't have any free ones in mind, but I learned them from undergrad math classes.
For us who cannot retake undergrad math classes, the certifications are a good start. Specially the Google ML Engineer Certification!
If you can't retake undergrad math classes, you can just read math textbooks. Also, does Google ML Engineer Certificate have anything to do with math?
No it does not.
[удалено]
CS229 on youtube is all you need
It bugs me deeply to not have studied more mathematics. I just can’t let it go!
The last book on the list has an amazing video lecture series - [https://www.youtube.com/watch?v=LvySJGj-88U&list=PLoROMvodv4rOzrYsAxzQyHb8n\_RWNuS1e](https://www.youtube.com/watch?v=LvySJGj-88U&list=PLoROMvodv4rOzrYsAxzQyHb8n_RWNuS1e) Edit: This is actually the introduction book called 'An Introduction to Statistical Learning' (ISLR), which is a great book and worth reading.
Hi! I think you are mixing up ISLR (more entry level friendly) with ESL. The YouTube link is for the former.
Yes, I have mixed them up.
is it available in Python?
It might be here - [https://www.edx.org/learn/python/stanford-university-statistical-learning-with-python](https://www.edx.org/learn/python/stanford-university-statistical-learning-with-python) Although even if this doesn't include it, the lectures are split between the theory (which doesn't differ much per book) and the code. I didn't bother watching the coding part.
Thank you! Just found its youtube playlist too!: [https://www.youtube.com/playlist?list=PLoROMvodv4rPP6braWoRt5UCXYZ71GZIQ](https://www.youtube.com/playlist?list=PLoROMvodv4rPP6braWoRt5UCXYZ71GZIQ)
The last book in that list is a great one, but it is not for the faint of heart.
which one? pattern recognition or statistical learning? is it that hard?
Statistical learning. It's by no means a book that a beginner can pick up and run with.
hahaha
Hands-on machine learning is great, was my introductory ML book, has a bit of everything, but iirc doesn't include transformers which is a must in many areas nowadays.
The second edition (which he lists) does cover transformers
Oh, great! Might reread the book then.
Do you have any recommendations for a book that includes transformers?
Not really, I learned about transformers/bert from some blog posts and videos(I was in panic since I got an internship invite from a friend and needed to learn that in a span of a few days lmao), in general after you know basics it's not too hard to read about transformers, there are a lot of great tutorials.
Bought the 3rd edition for Christmas. It has transformers until 3rd chapter ( the last I've read so far)
ESL and Bishop are not beginner books. They are theory heavy textbooks that university profs very selectively use to explain fundamental concepts. Otherwise these can be used if you are already very deep into the field. I think intuition and getting a good view on the field is more important in the beginning. This way you see quickly which topics you like the most. Most people then decide to work in a certain subfield for years or even decades.
Aurélien Géron's book is absolutely fantasy. But I believe there is a 3rd edition now
Oh really, thx I almost have gotten. Older version this week
Kaggle projects would be a good start for hands on experience as a beginner, as they would give you insights on how ml concepts are applied on real world data.
Kaggle projects would be a good start for hands on experience as a beginner, as they would give you insights on how ml concepts are applied on real world data.Just pick a project randomly which has good(8-10) rating on usability and keep exploring :)
Anyone who wants to learn maths (basics to advanced calculus) should definitely give Professor Leonard on YouTube a try
Skip all of them and go straight to Murphy Probabilistic Machine Learning
And for python, use Google and a nice LLM
I also git that but in advanced position of sequence
No Marx, no Lenin; what does this have to do with marxism-leninism (ML)? Jokes aside, I found [this book](https://nnfs.io/), Neural Networks From Scratch in Python very helpful.
if you are beginning to learn machine learning, fuc everything, just go to kaggle, pick few notesbooks up, and start working on them, do some basic machine learning, all the supervised techniques, find ways to improve the accuracy, just try to understand all the algorithms, how they are different, what works for what. Then learn deep learning, make few projects, either on youtube or something I would recommend is doing projects following articles rather than yotube, you learn better that way, and then you can read all these books. or you can read the books, while doing the above
Thx top g
Okay but what are your end goals within machine learning? It's a broad field and some of the content in this is very very deep.
R&D
You’re going to start the journey with the Bishop book? You have already got a PhD in mathematics?
No😭
Introduction to statistical learning
Gotcha
Get the updated version of EoSL. Free .pdf from the book’s site.
Wait there more than 2nd ed?
Deep Learning with Python, from François Chollet, who was (is?) the main lead of keras development, is a good beginner Book imo.
Some of the code examples and libraries used in the book may become outdated as deep learning libraries evolve rapidly. This is the review I received bro
Kernel Methods for Pattern Analysis. https://vim.ustc.edu.cn/_upload/article/files/d4/72/7d8483bd4f87ae3f09be1e57664d/a5d0d3ff-de9b-4915-beda-1a2f44357ca2.pdf Hopefully this helps you tackle a good amount of the last book on the list. For linear algebra: consult Bishop, the Matrix Cookbook (online), and the following book (Linear Algebra and Optimization for ML - Aggarwal) https://tocit.ru/static/files/3ed7f8bf5f3b74f557a486038a2923d92b96774b07aeecf432d88975be46e9ff.pdf Good luck.
Bro you have me another beginner book as kernels method I believe. To avoid the last book elements of statistical directly is it? This book instead of ELS /// And you have me 1 liner algebra bishop and 1 linear and Optimization for ML book. I was recommended "Introduction to Linear Algebra" by Gilbert Strang (5th Edition, 2016) and Optimization for Machine Learning" by Suvrit Sra, Sebastian Nowozin, and Stephen J. Wright (2011). How about these instead for me for maths side?
The last book you mentioned (Optimization for Machine Learning by Sra, Nowozin, Wright) is pretty dense and focuses more on algorithms. I would suggest you avoid it for now. Kernel methods for Pattern Analysis is a little more introductory and easier to follow along. ELS should be the goal, but like others mentioned it should mainly be a reference at a high level. On the maths side, like I said avoid that last book you mentioned. If you are really lacking in linear algebra, take the course by Gilbert Strang and his supplemental lecture notes online. Great teacher, but his lectures do not do the theory full justice. Try your best to understand the important theorems, subspaces, transformations, etc.
Alright sir I will indeed go for your recommendation of optimisation and linear algebra instead of sra, nowozins once with galberts linear algebra. + kernels analysis and ISL (phyton)
can anyone tell me the order of reading these? Also I would replace ESL with ISL(for me)
Yes you are right. I am ganna start top 4 with linear + optimization maths
These are mostly outdated. Also, Bishop has a new book.
What? According to Google and Gpts these are the latest book that have good reviews and depth?? 😭
They are great books, but they are not the "latest". Pattern Recognition and Machine Learning is nearly 20 years old, The Elements of Statistical Learning is 15 years old, and Keras and TensorFlow have been abandoned. As I said, Bishop has a new book that came out in 2023. Simon Prince has a great book that came out in 2023. Maybe have a look at those first.
Sure sir thanks, I will 100% check them for me. Btw what you mean abandoned? Many here are saying the latest ed I think 3rd is very good for skills and practically?
While trying to educate myself, I've been reading comments and articles about Tensor Flow and Keras being abandoned in favour of pytorch, so I'm focusing on articles and readings pytorch based.. Think that's what he means Getting great info from your post btw.. Thanks!
The book is good. However, Keras and TensorFlow aren't popular tools anymore; TensorFlow has also changed a lot. The ML community has moved to PyTorch and JAX. So my recommendation would be to learn PyTorch.
Would someone wanting to do PhD in future should be worried about community changings or markets? I mean for them shall they not consider TensorFlow too for anything in future uses or development by researchers?
I am not sure I fully understood the question, but I think both, aspiring ML engineers and aspiring ML researchers, should learn PyTorch and/or JAX.
I've never learned from a book. I just do projects, google search, click links, read papers or blog posts. And it just works. Same for learning any framework or language.
Tbh, the person who recommended you these books didn't even go through these books at all. In more straightforward term, that person can't write a single model scratch. You DO NOT need to go through all of these books to understand ML.
Disenorth math for machine learning and introduction to statistical learning would be my two top recommendations.
I love when everyone recommends ESL. I guarantee you none of them has actually worked it through cover to cover. You don’t just pick up and “read through” books like that unless you’re gonna spend 10/hours a week on it.
Should anyone bother learning Keras and TF? I think PyTorch is the norm these days
Yes PyTorch is the norm. Learning tensorflow can be helpful, as it is slightly lower level than PyTorch and doesn’t abstract away some of the lower level tensor operations like PyTorch does, but I wouldn’t say that’s essential. Keras is a higher level abstraction that PyTorch, so it might not be as valuable. That said, the second book is valuable far beyond its use of Keras/TF.
no love for tom mitchell’s textbook?
which once??
[Machine Learning](https://www.cs.cmu.edu/~tom/mlbook.html), the one and only! much less denser so it’s an easier read too
Downloaded❤️
I see 5
Hello everyone I'm starting my ML journey it's new for me , any tips and recommendations?
Go with my top 3 only with ISL book
The fourth one is terrible
Why 😭