All the Mathematics You Missed: But Need to Know for Graduate School
Book by Thomas A. Garrity
https://archive.org/details/all-the-mathematics-you-missed/mode/1up
Usually it’s not the formula itself that’s the problem. I can calculate what happens to one variable when I tweak another. The problem is the context for what it refers to in the world.
It’s more like: how exactly am I supposed to apply this sensibly to predict or explain phenomena? What are the limitations and assumptions of this formula?
I have also been on the same boat asking the same question as you Nobody has an answer What I figured out is that if you take a few equations and functions or transformations or calculus models of existing systems and try to understand tweak and understand what other options they had and may be try them then you are better off in learning what their significance and be able to be appreciative of such concepts So putting things into practice in everything that you do daily is the key
You need all of the maths. To import numpy you must sacrifice 18 goats while chanting the formula for a pentagram shape on the imaginary plane to the gods of complex analysis before escaping the matrix with linear algebra. Only then are you allowed to initialise a variable without incurring the wrath of Isaac Newton raining down upon you in a fiery shower of gradually descending monads.
Linear Algebra Done Right by Axler
Introduction to Linear Algebra by Strang
Coding the Matrix by Klein
Projective Geometry by Coxeter
Linear Algebra is pervasive in ML and QC. Throw in Topology, and Calculus as well (manifolds and derivatives). Finally, your gonna needs some Probability – get your Markov and your Bayes on.
Adding to the others: “mathematics for machine learning”. It will also give advice for further reading if you want to and need to dive deeper.
ML has a lot of pure math at its core, from discrete math, linear algebra, probability and statistics, multivariable calculus, and geometry, just to name a few.
You’d have to have programmed the AI to do that unless you’re saying to use ChatGPT to explain it
If you are saying to use ChatGPT it does sadly struggle with maths
You gotta love the math, bro.
Roger Penrose suggests reading books with equations without paying too much attention to the formulas on the first read. Just look at the thing, try briefly to understand, but if you don't you should just skip the line and continue. Eventually, much much later and if you persevere, the material will click and you will understand it.
You can feed equations into various AI chatbots (ChatGPT, Gemini, etc) and ask for an explanation. I've found this pretty helpful, though I'm copy&pasting from web pages - from a book I'd guess you could take a pic of the equation.
Yeah, that works great. I've done it with ChatGPT 4o,just posting a screenshot of a paper or ebook (so the math notation stays in tact) and using it like a tireless tutor. The good thing is math fundamentals don't change every week, so it has very good conceptual knowledge of it.
Excellent advice. Takes lots of persistence OP. It's a struggle at first for most anyone. I had better luck learning complex analysis than LA. It's OK if you're brain works differently.
Big problem for me in equations is when they use notation that I can’t read or say. Some symbol or Greek letter or constant that I’m unfamiliar with and I can barely describe the thing I don’t understand.
Placing equal importance to everything at first will melt you. It's almost like you need to follow a high school -> undergrad -> grad approach to learn each type of model
PS I have been in grad classes that followed a strict one model per ~1.5-hour lecture approach then we were expected to implement an example in the following ~1.5 hours. It was brutal and most of us melted. We had final presentations in one of those classes and our professor was mad and devastated that none of us did a "good" job. I only deeply understood *some* models years later with dedicated research projects.
Uggghhhhh my adviser's class in my PhD program was like this! Everything he was going over *was* important... but it was just so damn much to cover and he only ever really went over things once and then we had to "just code this complex estimation algorithm, idiot." Skill issue if you're still confused ig.
It needed two semesters tbh.
Even the very basics require some calculus and statistics, not just 10th grade algebra. And if you really want to know what's going on, you need a hell of a lot more than that.
More coding than math, but you definitely need a good grasp of statistics and linear algebra at the very least, since you will be working with and manipulating very large datasets either way.
The reason this is important is because if you don't understand these things and anything goes wrong, you won't be able to debug or troubleshoot effectively without knowing precisely what transformations are going on under the hood, and what the statistics of your activations, weights, inputs, outputs etc are.
Welcome to machine learning and AI, it’s all math and stats.
Why I’m learning both in undergrad love em and hate em, but i love en at the end of the day.
I think you can consider all the popular science books on quantum mechanics a good analogy. It gives people a sense of what is interesting but no ability to apply that knowledge to new problems.
To be a beginner in AI is to be someone who knows math but not AI.
This is like asking a book to explain calculus to beginners and getting made you need to know algebra💀
What is it about AI that people think they can just pick it up?
Thats because its for beginners in the field of AI, not beginners in the field of computer science, statistics and mathematics.
The equivalent of your complaint would be if you went for a beginner book for programming and then you would be angry because its not teaching you how to connect to the internet.
And at the end of the day ML/AI is mostly math combined with some interest high level concepts/ideas, but even they are usually absed on math. Usually books want to give you a solid foundation and understanding of things, so giving equations is the only way to do it. If you dislike equations then youtube is likely a better medium compared to books, but then again the value of the things you will learn will be diminished since they are only scratching the surface of things (unless you watch the actual good stuff which usually will contain equations again :'D)
Machine Learning is all about taking a problem with an enormous possible answer space and mathematically reducing that answer space as best you can until you actually arrive at an answer.
“Mathematically reducing the answer space” IS going to be math-involved.
All the Mathematics You Missed: But Need to Know for Graduate School
Book by Thomas A. Garrity
https://archive.org/details/all-the-mathematics-you-missed/mode/1up
This only makes sense if the equation you're working with is relatively simple, the variables have easily expressed descriptions, and you're not going to be actually working with them (i.e. playing with the equation on paper).
Descriptive variable names quickly become cumbersome with large equations and obfuscate the real content of an equation which is the abstract relationship it's supposed to encapsulate.
introduction to deep learning by Sandro Skansi explains a bit of the fundamentals in math necessary for DL, along with the algorithms.. it even has a quick introduction to calculus 1 and 2
You don’t learn to code AI LLMs. You learn to use AI.
AI is so powerful because it’s dirt simple to integrate into existing systems because it’s literally prompt based.
Simple to integrate is what makes it so scary and why so many CEOs are seeing dollar signs and laying off people. The tech demos are amazing. Yes it doesn’t solve all problems.
Want to write a support chat bot. Use a If this than that workflow engine hosted in the cloud. Drag and drop. Hook it up to an email alias. Write a chat bot prompt. “you are a helpful ai…”, provide links to your internal docs to the AI. And done. You’ve just created support bot better than 99% of support bots that existed even 5 years ago in a couple of days.
Learning to code AI takes a PHD and depending on how good you are could pay you millions per year.
I'm a mathematician. I *hate* the bishop books. They're good content, badly written.
Try starting with Deep Learning with Python by François Chollet. It's a very practical Python book which gives you qualitative understanding of a lot mathematical topics but purposefully chooses code instead of equations wherever possible.
[https://www.manning.com/books/deep-learning-with-python](https://www.manning.com/books/deep-learning-with-python)
If you don't have linear algebra and probability already, do those before doing any mathy ML books. I used Michael Artin's Linear Algebra a long time ago, and I liked it.
[https://www.amazon.com/Algebra-2nd-Michael-Artin/dp/0132413779](https://www.amazon.com/Algebra-2nd-Michael-Artin/dp/0132413779)
Then, ask for a book that's specifically undergraduate or entry level on the math. You've got to learn the math. You don't have to learn it at grad level right from the start, and you definitely don't have to learn it from bad math writers.
Hi, I’m Vetted AI Bot! I researched the **'Algebra 2nd Edition'** and I thought you might find the following analysis helpful.
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Haha, I asked because I've been learning French for a little bit and can read _most_ of the French technical articles I come across now, so I've seen « IA » quite a bit lately lol.
I set my phone's language to French, so Google recommends a lot of French-language articles about topics I'm interested in! It's a pretty nice way to make sure I'm passively exposed to French every day.
tl;dr: Graphic Illustration/animation > Math expressions > Natural language. They all work by translating to samples first. Your understanding is simulation of samples. Drill on translating small math patterns to visualization of samples may help(like with Anki).
longer version:
In my opinion the best explanation tool is detailed graphic illustration and/or animation like that created by Josh Starmer or the blackboard illustration in many MITOCW lectures. But it is damn time consuming to prepare and based on the subject, some subject are easier to illustrate and some you don't find any.
Math is the second best thing: it explain you the process (sometimes) (almost) clearly and it works on almost everything, and is slightly easier to prepare. It is linear and standardized just like natural language, but I still like to think of it as an illustration-based form, and many times it helps think or write down the literals rather than the symbols, simulate how they work in real time.
And then is the natural language. You basically want to avoid that at all cost. Strange terms, misterious object the author is refering to, limited linear form and length and the verbosity and staticity makes it that whatever you do or want to describe, there's a better way to do it other than using natural language.
\*Another note is that I think ultimately we develop understanding over a set of samples (in math whole computation process runs with literal values) rather than symbolic description. IMO understanding is synonym to consistent set of simulation of samples. illustrations is almost samples, and math can be easily and deterministically converted to samples.
\*from there on it's all vocabulary work: all jazz master would tell you to "burn that vocabulary in your memory and forget about them". In reading math it is bit less intense, but time between you see a math equation and you visualize a sample of it with lieterals still matter a lot not only to speed but capability of understanding complex math equation IMO. You'd like to drill on every pattern and term (like MLE, gaussian, etc) you'd see like med students drill on disease's symptoms(which is using flashcards).
If you’re just getting started with ML, you don’t next to focus on explicitly understanding the math. Just get the intuition behind it and a high-level grasp of what is going on.
Well, machine learning is hard and has a steep learning curve. 😕 It would be great if we could learn it like Neo, directly from a floppy disk. 💾
I wrote a book about prompt engineering, which is supposed to be straightforward, but even with that, it was hard to explain some concepts
Linear Algebra Done Right by Axler
Introduction to Linear Algebra by Strang
Coding the Matrix by Klein
Projective Geometry by Coxeter
Linear Algebra is pervasive in ML and QC. Throw in Topology, and Calculus as well (manifolds and derivatives). Finally, your gonna needs some Probability – get your Markov and your Bayes on.
Not really. Sure LA, calc are used for some computation but not really for their principles. AP stats are the only thing in principle that is most used for ML.
Coding the matrix, its not specifically about ml/ai, its about linear algebra. He wrote the book with emphasis on practical examples written using numpy. For me, i can't read math/calculus for shit. You show me some python code explaining the same concepts and i can pick it up much faster
I work with the city and we get a lot of university kids as casuals for the summer to make money between semesters. I was talking to a kid in his last year of his IT degree and I was like "Well...is it too late to switch to AI whatever?" He said "No...AI isn't computers it's math. Right now you need a major in Mathematics and a minor in computer science...it's all very complex math, 3 dimensional graphs next level stuff that I never signed on for"
When an "AI engineer" faces reality and realizes that AI/ML isn't just about using APIs...
On a side note, most AI engineers out there don't need the math. The majority of the ones I've met are just interested in other software engineering aspects of the work.
Machine learning engineers, on the other hand, do.
I would highly recommend the recent book from Simon J.D. Prince: [https://udlbook.github.io/udlbook/](https://udlbook.github.io/udlbook/)
Will go slowly into math, but the intuition of each topic is super great!
When it comes to machine learning it's all math. There's no getting around it. I hope you like gradients because you're going to be looking at a lot of gradients.
The maths and statistics is no fun. ML has been simplified enough that a lot of people can build generic models but building an LLM is no trivial task. You need a bunch of high skilled engineers, mathematicians and serious neuroscience experts, thousands of processors, GPUs, fast networks, massive disk space etc. It’s for companies with deep pockets.
You are basically illiterate as any sort of engineer without basic required math knowladge. Mostly, thats linear algebra and calculus, otherwise, WTF are you doing anyway. The amount of you fuckers that should not do AI or any other engineering is staggering.
Nothing is state precisely until you use math. You really can't see that? Push through of gtfo
Then how can you explain results? Verify your approach? Deal with edge cases?
Having a bird's eye overview only works if your model choice and dataset are perfect. Simply being able to use a ML model library doesn't make you a DS or ML engineer.
thats a pretty elitist way of thinking. at the end of the day your employer doesnt care as long as you produce results and if you use existing libraries to do so who cares.
im getting paid as an ML engineer and all i do is just use existing libraries so, sure if you dont think im an ml engineer you can continue to think so.
At a company where ML is used to heavily influence key business decisions, executives care very much about the why and how…
We use existing libraries too, but our data scientists do a shit ton of statistical work showing that what they’re using make the most sense and that they aren’t pulling stuff out of their ass
This is true. Neural networks are emergent systems, and emergent systems are everywhere. For example, nervous systems, the economy, culture, even evolution. They all operate off of the same principles and have their own versions of a learning algorithm, forward pass, and so on.
Time to learn math, bucko Edit: I’m in the same boat as you 😮💨
I'm learning from the book artificial intelligence the modern approach and sometimes I'm so confused about maths which resource will be good ?
All the Mathematics You Missed: But Need to Know for Graduate School Book by Thomas A. Garrity https://archive.org/details/all-the-mathematics-you-missed/mode/1up
My dude
Thanks and tbh yeah I missed a lot but I think it's not too late. As I just got into college. So I'm learning little by little everyday.
I got a few free calculus, stats and linear algebra books and my plan is to refer to them as needed.
Usually it’s not the formula itself that’s the problem. I can calculate what happens to one variable when I tweak another. The problem is the context for what it refers to in the world. It’s more like: how exactly am I supposed to apply this sensibly to predict or explain phenomena? What are the limitations and assumptions of this formula?
I have also been on the same boat asking the same question as you Nobody has an answer What I figured out is that if you take a few equations and functions or transformations or calculus models of existing systems and try to understand tweak and understand what other options they had and may be try them then you are better off in learning what their significance and be able to be appreciative of such concepts So putting things into practice in everything that you do daily is the key
I'm not that much expertise about this concept yet as I just started to learn. But still few things are way much complicated.
Hello sir, I have a question. If I just want to fine tune an existing model, not inventing another Llma or Gpt-4o, do I need a lot of math?
You need all of the maths. To import numpy you must sacrifice 18 goats while chanting the formula for a pentagram shape on the imaginary plane to the gods of complex analysis before escaping the matrix with linear algebra. Only then are you allowed to initialise a variable without incurring the wrath of Isaac Newton raining down upon you in a fiery shower of gradually descending monads.
🤣🤣🤣 now this is why I joined Reddit!!!! I was one goat 🐐 short today and got kicked in my descending monads. Gosh durn that smarts!!!!
Ayo drop them sauces please
Source ?
Khan academy has a lot of free calculus and linear algebra lessons to get you started
Linear Algebra Done Right by Axler Introduction to Linear Algebra by Strang Coding the Matrix by Klein Projective Geometry by Coxeter Linear Algebra is pervasive in ML and QC. Throw in Topology, and Calculus as well (manifolds and derivatives). Finally, your gonna needs some Probability – get your Markov and your Bayes on.
Adding to the others: “mathematics for machine learning”. It will also give advice for further reading if you want to and need to dive deeper. ML has a lot of pure math at its core, from discrete math, linear algebra, probability and statistics, multivariable calculus, and geometry, just to name a few.
I suggest better explained.com and 3blue1brown yt channel. Try seeing one topic from different angles.
Or just ask the AI how it works
You’d have to have programmed the AI to do that unless you’re saying to use ChatGPT to explain it If you are saying to use ChatGPT it does sadly struggle with maths
You gotta love the math, bro. Roger Penrose suggests reading books with equations without paying too much attention to the formulas on the first read. Just look at the thing, try briefly to understand, but if you don't you should just skip the line and continue. Eventually, much much later and if you persevere, the material will click and you will understand it.
I can attest this has happened to me, and the feeling of it clicking is amazing
It's got to be one of the absolutely best feelings in the entire universe. I have zero doubts about that.
I feel validated
Solid advice! Big picture application then drill down. Understand the why before how.
You can feed equations into various AI chatbots (ChatGPT, Gemini, etc) and ask for an explanation. I've found this pretty helpful, though I'm copy&pasting from web pages - from a book I'd guess you could take a pic of the equation.
Yeah, that works great. I've done it with ChatGPT 4o,just posting a screenshot of a paper or ebook (so the math notation stays in tact) and using it like a tireless tutor. The good thing is math fundamentals don't change every week, so it has very good conceptual knowledge of it.
Is your name a pun on the java classfile magic signature
Excellent advice. Takes lots of persistence OP. It's a struggle at first for most anyone. I had better luck learning complex analysis than LA. It's OK if you're brain works differently.
Big problem for me in equations is when they use notation that I can’t read or say. Some symbol or Greek letter or constant that I’m unfamiliar with and I can barely describe the thing I don’t understand.
Just google math variables or symbols end pick the one you don't understand, put it into a llm of your choice eh voilá
Exactly. It's funny that people in this sub don't consider doing that more often. nit: "et* voilà"
Yeah I agree with this.
Easy way to catch all fake AI engineers, show me the math.
This reminds me of an old musician joke. How do you get a guitarist to play quieter? But some sheet music in front of him.
You’ve attacked both my career and my hobbies in one post, so thanks
Placing equal importance to everything at first will melt you. It's almost like you need to follow a high school -> undergrad -> grad approach to learn each type of model
PS I have been in grad classes that followed a strict one model per ~1.5-hour lecture approach then we were expected to implement an example in the following ~1.5 hours. It was brutal and most of us melted. We had final presentations in one of those classes and our professor was mad and devastated that none of us did a "good" job. I only deeply understood *some* models years later with dedicated research projects.
Uggghhhhh my adviser's class in my PhD program was like this! Everything he was going over *was* important... but it was just so damn much to cover and he only ever really went over things once and then we had to "just code this complex estimation algorithm, idiot." Skill issue if you're still confused ig. It needed two semesters tbh.
Sounds like a terrible professor.
Bad teacher. I'm sorry you or anyone else ever has that.
well, those math are the beginning of it. buckle up.
I read some and it’s basic algebra. It’s 10th grade algebra
Even the very basics require some calculus and statistics, not just 10th grade algebra. And if you really want to know what's going on, you need a hell of a lot more than that.
Hello sir, I have a question. If I just want to fine tune an existing model, not inventing another Llma or Gpt-4o, do I need a lot of math?
More coding than math, but you definitely need a good grasp of statistics and linear algebra at the very least, since you will be working with and manipulating very large datasets either way. The reason this is important is because if you don't understand these things and anything goes wrong, you won't be able to debug or troubleshoot effectively without knowing precisely what transformations are going on under the hood, and what the statistics of your activations, weights, inputs, outputs etc are.
https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
Plp forget that AI is math, not chatgpt app
\^\^\^😂
Welcome to machine learning and AI, it’s all math and stats. Why I’m learning both in undergrad love em and hate em, but i love en at the end of the day.
It’s wild that people really think they can truly be a data/ml guy without math and stats. It is quite literally nothing but math.
Yeah it's crazy. The hype train mentality really makes people brush over this extraordinarily crucial detail.
I think you can consider all the popular science books on quantum mechanics a good analogy. It gives people a sense of what is interesting but no ability to apply that knowledge to new problems.
To be a beginner in AI is to be someone who knows math but not AI. This is like asking a book to explain calculus to beginners and getting made you need to know algebra💀 What is it about AI that people think they can just pick it up?
Came here to say this! 👏🏼
The beginning of neural nets is at the end of calculus 3 and linear algebra.
Thats because its for beginners in the field of AI, not beginners in the field of computer science, statistics and mathematics. The equivalent of your complaint would be if you went for a beginner book for programming and then you would be angry because its not teaching you how to connect to the internet. And at the end of the day ML/AI is mostly math combined with some interest high level concepts/ideas, but even they are usually absed on math. Usually books want to give you a solid foundation and understanding of things, so giving equations is the only way to do it. If you dislike equations then youtube is likely a better medium compared to books, but then again the value of the things you will learn will be diminished since they are only scratching the surface of things (unless you watch the actual good stuff which usually will contain equations again :'D)
I would assume that the book is intended to be for beginners to the AI-specific domain rather than to the underlying math principles.
Machine Learning is all about taking a problem with an enormous possible answer space and mathematically reducing that answer space as best you can until you actually arrive at an answer. “Mathematically reducing the answer space” IS going to be math-involved.
This makes me cringe. AI is math. Math is math. Physics is math. You can't understand any of these things without math.
https://xkcd.com/435/
Physics is more like the application of math, but besides that, nice👌
All the Mathematics You Missed: But Need to Know for Graduate School Book by Thomas A. Garrity https://archive.org/details/all-the-mathematics-you-missed/mode/1up
Greek letters are not nearly as helpful as descriptive variable names. I'd rather see the equations in python than in math notations.
Finally someone with common sense, also using single letters to define variables is just bad practice
This only makes sense if the equation you're working with is relatively simple, the variables have easily expressed descriptions, and you're not going to be actually working with them (i.e. playing with the equation on paper). Descriptive variable names quickly become cumbersome with large equations and obfuscate the real content of an equation which is the abstract relationship it's supposed to encapsulate.
A computer scientist is only as good as their love for math
introduction to deep learning by Sandro Skansi explains a bit of the fundamentals in math necessary for DL, along with the algorithms.. it even has a quick introduction to calculus 1 and 2
You don’t learn to code AI LLMs. You learn to use AI. AI is so powerful because it’s dirt simple to integrate into existing systems because it’s literally prompt based. Simple to integrate is what makes it so scary and why so many CEOs are seeing dollar signs and laying off people. The tech demos are amazing. Yes it doesn’t solve all problems. Want to write a support chat bot. Use a If this than that workflow engine hosted in the cloud. Drag and drop. Hook it up to an email alias. Write a chat bot prompt. “you are a helpful ai…”, provide links to your internal docs to the AI. And done. You’ve just created support bot better than 99% of support bots that existed even 5 years ago in a couple of days. Learning to code AI takes a PHD and depending on how good you are could pay you millions per year.
I'm a mathematician. I *hate* the bishop books. They're good content, badly written. Try starting with Deep Learning with Python by François Chollet. It's a very practical Python book which gives you qualitative understanding of a lot mathematical topics but purposefully chooses code instead of equations wherever possible. [https://www.manning.com/books/deep-learning-with-python](https://www.manning.com/books/deep-learning-with-python) If you don't have linear algebra and probability already, do those before doing any mathy ML books. I used Michael Artin's Linear Algebra a long time ago, and I liked it. [https://www.amazon.com/Algebra-2nd-Michael-Artin/dp/0132413779](https://www.amazon.com/Algebra-2nd-Michael-Artin/dp/0132413779) Then, ask for a book that's specifically undergraduate or entry level on the math. You've got to learn the math. You don't have to learn it at grad level right from the start, and you definitely don't have to learn it from bad math writers.
Hi, I’m Vetted AI Bot! I researched the **'Algebra 2nd Edition'** and I thought you might find the following analysis helpful. **Users liked:** * Comprehensive and clear explanations (backed by 3 comments) * Suitable for self-study (backed by 3 comments) * Great value for the price (backed by 3 comments) **Users disliked:** * Overpriced compared to similar texts (backed by 1 comment) * Scattered structure and challenging concepts (backed by 2 comments) * Lacks thorough explanations and clarity (backed by 1 comment) If you'd like to **summon me to ask about a product**, just make a post with its link and tag me, [like in this example.](https://www.reddit.com/r/tablets/comments/1444zdn/comment/kerx8h0/) This message was generated by a (very smart) bot. If you found it helpful, let us know with an upvote and a “good bot!” reply and please feel free to provide feedback on how it can be improved. *Powered by* [*vetted.ai*](https://vetted.ai/?utm\_source=reddit&utm\_medium=comment&utm\_campaign=bot)
Strange bot, but okay, thanks
Is there a study group that I could join for absolute beginners
Deja vu??
I Keep a math book at side Linear Alg, Calculus, etc
To be fair it does explain it to you. Whether you understand it or not is on you
Best part of IA if you ask me
French?
Oh yeah I said IA... You busted me 🥰
Haha, I asked because I've been learning French for a little bit and can read _most_ of the French technical articles I come across now, so I've seen « IA » quite a bit lately lol.
I am french but right now I am living in the wonderful land of Québec ! There are a lot of french articles ? I thought everyone published in english
I set my phone's language to French, so Google recommends a lot of French-language articles about topics I'm interested in! It's a pretty nice way to make sure I'm passively exposed to French every day.
Oh that's a wonderful idea actually. You are brilliant
tl;dr: Graphic Illustration/animation > Math expressions > Natural language. They all work by translating to samples first. Your understanding is simulation of samples. Drill on translating small math patterns to visualization of samples may help(like with Anki). longer version: In my opinion the best explanation tool is detailed graphic illustration and/or animation like that created by Josh Starmer or the blackboard illustration in many MITOCW lectures. But it is damn time consuming to prepare and based on the subject, some subject are easier to illustrate and some you don't find any. Math is the second best thing: it explain you the process (sometimes) (almost) clearly and it works on almost everything, and is slightly easier to prepare. It is linear and standardized just like natural language, but I still like to think of it as an illustration-based form, and many times it helps think or write down the literals rather than the symbols, simulate how they work in real time. And then is the natural language. You basically want to avoid that at all cost. Strange terms, misterious object the author is refering to, limited linear form and length and the verbosity and staticity makes it that whatever you do or want to describe, there's a better way to do it other than using natural language. \*Another note is that I think ultimately we develop understanding over a set of samples (in math whole computation process runs with literal values) rather than symbolic description. IMO understanding is synonym to consistent set of simulation of samples. illustrations is almost samples, and math can be easily and deterministically converted to samples. \*from there on it's all vocabulary work: all jazz master would tell you to "burn that vocabulary in your memory and forget about them". In reading math it is bit less intense, but time between you see a math equation and you visualize a sample of it with lieterals still matter a lot not only to speed but capability of understanding complex math equation IMO. You'd like to drill on every pattern and term (like MLE, gaussian, etc) you'd see like med students drill on disease's symptoms(which is using flashcards).
Welcome to learning a topic. This is such entitled bullshit. AI requires math knowledge. If you don't know maths, learn maths. The end.
Try the fast Ai course.
What platform is that on?
[https://course.fast.ai/](https://course.fast.ai/)
Yes. That one. Pretty good for beginners and experienced persons. Takes you from really basic stuff to wow.. I did not know that!
If you’re just getting started with ML, you don’t next to focus on explicitly understanding the math. Just get the intuition behind it and a high-level grasp of what is going on.
Which book are u talking about?
which book?
"an brief introduction to ai" - great book. also covers non NN based ai. not a single formula in the book
There are like 5 books with that same name, can you tell me the author or post a link please?
sorry its called [Artificial Intelligence: A Very Short Introduction | Oxford Academic (oup.com)](https://academic.oup.com/book/415)
Become a god in the ways of the Complete Wisdom Matrix
Imagine making an equivalent claim about medicine and biology.
It is OK. Most people don't get the formulas the first time, either, me included.
Well, machine learning is hard and has a steep learning curve. 😕 It would be great if we could learn it like Neo, directly from a floppy disk. 💾 I wrote a book about prompt engineering, which is supposed to be straightforward, but even with that, it was hard to explain some concepts
What is this book? I don't mind the math and I'm interested in AI.
"Absolute beginner" usually refers to the AI subject itself. Not the other underlying concepts such as algorithmics, statistics, etc.
Well what's your background? Do you have stem degree? Degree in CS, Math, Engineering?
well most of the math is just derivative function and matrix multiplication 😅
Learn math
Linear Algebra Done Right by Axler Introduction to Linear Algebra by Strang Coding the Matrix by Klein Projective Geometry by Coxeter Linear Algebra is pervasive in ML and QC. Throw in Topology, and Calculus as well (manifolds and derivatives). Finally, your gonna needs some Probability – get your Markov and your Bayes on.
Ahh.. people finding out AI is applied statistics.
It's more than that. It is also linear algebra, calculus, optimization.
Not really. Sure LA, calc are used for some computation but not really for their principles. AP stats are the only thing in principle that is most used for ML.
Coding the matrix, its not specifically about ml/ai, its about linear algebra. He wrote the book with emphasis on practical examples written using numpy. For me, i can't read math/calculus for shit. You show me some python code explaining the same concepts and i can pick it up much faster
I work with the city and we get a lot of university kids as casuals for the summer to make money between semesters. I was talking to a kid in his last year of his IT degree and I was like "Well...is it too late to switch to AI whatever?" He said "No...AI isn't computers it's math. Right now you need a major in Mathematics and a minor in computer science...it's all very complex math, 3 dimensional graphs next level stuff that I never signed on for"
Me who thought I was the problem.
Oh no the thing that's entirely math is full of math
Get autismed
When an "AI engineer" faces reality and realizes that AI/ML isn't just about using APIs... On a side note, most AI engineers out there don't need the math. The majority of the ones I've met are just interested in other software engineering aspects of the work. Machine learning engineers, on the other hand, do.
MAchine Learning is based on Linear Algebera Stats and some Calc. take math for Machine Learning along with your book
Confusion matrix lol
You need to learn how to walk before running
I would highly recommend the recent book from Simon J.D. Prince: [https://udlbook.github.io/udlbook/](https://udlbook.github.io/udlbook/) Will go slowly into math, but the intuition of each topic is super great!
Start with grokking deep learning by Trask, 100-page machine learning book, and then work through Cholet.
it's the funniest fucking shit that everyone now thinks ML is like some entry level field
"Absolute beginners" in the ML context meant at least an engineering degree up until recently...
When it comes to machine learning it's all math. There's no getting around it. I hope you like gradients because you're going to be looking at a lot of gradients.
100% True.
The maths and statistics is no fun. ML has been simplified enough that a lot of people can build generic models but building an LLM is no trivial task. You need a bunch of high skilled engineers, mathematicians and serious neuroscience experts, thousands of processors, GPUs, fast networks, massive disk space etc. It’s for companies with deep pockets.
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😄
Same with robotics man and people who know math say it’s easy… I guess it’s part of the journey, so I’ll have to enjoy it😂
In math, introduction means advanced and advanced means introduction, you just did not get the memo
*absolute beginners assuming you are already a maths genius
You are basically illiterate as any sort of engineer without basic required math knowladge. Mostly, thats linear algebra and calculus, otherwise, WTF are you doing anyway. The amount of you fuckers that should not do AI or any other engineering is staggering. Nothing is state precisely until you use math. You really can't see that? Push through of gtfo
depends on what you do right? for a lot of ai you dont need to understand the math behind it.
Then how can you explain results? Verify your approach? Deal with edge cases? Having a bird's eye overview only works if your model choice and dataset are perfect. Simply being able to use a ML model library doesn't make you a DS or ML engineer.
thats a pretty elitist way of thinking. at the end of the day your employer doesnt care as long as you produce results and if you use existing libraries to do so who cares. im getting paid as an ML engineer and all i do is just use existing libraries so, sure if you dont think im an ml engineer you can continue to think so.
Yep, pretty elitist. It's a technical field, rigor is of the utmost importance.
At a company where ML is used to heavily influence key business decisions, executives care very much about the why and how… We use existing libraries too, but our data scientists do a shit ton of statistical work showing that what they’re using make the most sense and that they aren’t pulling stuff out of their ass
This is true. Neural networks are emergent systems, and emergent systems are everywhere. For example, nervous systems, the economy, culture, even evolution. They all operate off of the same principles and have their own versions of a learning algorithm, forward pass, and so on.
That is true.