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I took Deep Learning and Machine Learning subjects at the university last semester and now in the current semester. The Deep Learning professor would explain the theoretical process in plain English with graphical representations before even showing any math formulas. By that time, I get quite comfortable with what I'm learning or about to learn since I know the process and the end goal. The only thing I look for in the formulas is certain details or how exactly what is happening in each step if I'm too curious. On the other hand, my Machine Learning professor, if I attend the class, all I hear is math. When I go home, all I have to study from is the slides, which are again all math with no explanation in plain English. I ended up dropping the subject. It's crazy how a teacher can make someone love/hate a subject or make them feel good/bad at the subject

He forgot rule 1: talent for sitting and focusing for days without getting up for food or daylight

I gave up learning deep learning many times. Then in 2021 i had painful divorce. It changed everything instead of going into the depression i channeled my energy into learning deep learning. Now i am able to train small llms and text to speech models from scratch on multiple gpus. I am starting a company in the next month!

The day I stopped hammering formulas into my head, I understood how amazing math actually is! My advice to my younger self would be to try to understand the intentions behind all those formulas and not to force it. Occasionally, you discover why you need it when you need it.

Thanks for this (and also for the other videos) man! You can really see you're down to earth and you'va faced the same struggles everyone has in the journey towards becoming better ML scientists/practitioners!

This video is a breath of fresh air. I was frustrated because I couldn't understand how to use einops for 3 hours and I was only writing 1 line, I'm glad I stumbled upon this

Its like you have opened a new window for me into ML world. I was stuck at figuring out which topic falls into which category, but after listening to you it all came together. Now i can organize my thoughts properly while thinking and learning. I can relax while reading math formulas and be content by just understanding the intent behind it. I can relax while going through derivations knowing that it is pure mathematical realm and i can tackle it separately. And finally now i am slowly beginning to understand how to break down my tasks and work aka apply first principles thinking to ML problems. In short, you have helped me form a initial mental schema for ML. Thanks a lot !

Thank you so much for this Boris as has helped me so much as I am learning ML

I am reading it mate! It was actually a learning update. This year will be an intense core year of studying and personal projects.

The thing that gets me about Deep Learning are RNNs, then going through LSTMs and GRUs. I try to memorize what happens throughout the entire RNN with activation layers and backwards propagation through time. It is just a lot of over-convoluted complex stuff. I just started learning Machine Learning and Deep Learning in August of 2023. I am just struggling so much with it, but I love it. I love watching lectures on Transformer sequences with encoder and decoder even though at first a lot of it sounds like Ancient Greek to me. I will keep trying

Pretty good points. A couple comments from a 16+ year software engineer with a Master's in CS: - Understanding that a sum corresponds to a for loop is great, but in practice, it should still almost always be thought of as a function. You should think in self-documenting ways, and this will also lead to easier optimization later. For example, libraries that can vectorize functions like sum can perform operations on large datasets much faster than a for loop would. - Good advice for examining ML codebases, but more generally _follow the data flow._ Knowing how data flows through a system makes understanding the pieces along the way much easier. This is also a good way to estimate code quality. If the data flow of the system is easy to follow, it might be good code. If it's hard to follow, it's almost certainly poorly structured code, and changing or adding anything will be a struggle without refactoring first.

thanks brother, your videos are incredibly useful!

Thinking of different degrees. I saw and experienced statistics, computer science, physics, and cognitive science and contemplated the curricula at my university (Helsinki). Many times I felt these fields to go deep in their own direction, if one wants to go primarily towards ML or AI. Then the internet is full of learn X quickly types of content. However, what I get from your experience is, that perhaps your/some ML curricula is streamlined in a way to get optimal amount of depth and breadth specifically for ML. For me the big question is, how deep into math one should go, because prioritization gives you other skills too...

00:01 Understanding math the right way is crucial for learning machine learning. 01:39 Translate human ideas into math concepts 03:18 Understanding math derivation rules simplifies ML learning 05:02 Recognize patterns and practice is key to mastering ML. 06:40 Realizing that debugging is coding helps with implementation 08:18 Understanding large code bases with a simple strategy 09:56 Setting breakpoints in the main function for debugging is crucial in understanding ML implementations 11:41 Mastering ML takes time and dedication. Crafted by Merlin AI.

You speak some straight up truth in this video, these are super helpful insights into how to learn. One thing I would say is for programming you kind of go against the super good advice you give for learning maths. When trying to understand a big codebase, the debugger will not help -- the debugger is there as a magnifying glass into all the details. But just like your example in maths, code is just human ideas expressed a certain way, so understand the higher level ideas of functions or modules, and go deeper as your understanding improves. The debugger is great for when you are debugging the behaviour of your code, or if you are very familiar with the code someone else wrote.

Great video man, I learnt alot, I am just starting as an ML engineer myself

Thanks a lot for sharing the truth of learning machine learning. It was not less than a pain killer.

Hello! I really admire the way you teach something. Maybe we can have a sample tutorial of a ML / DL project with the standard structure for industry level

Nice video, agree with the points. I think your last point is particularly important. Patience! It takes time to learn complex things. We live in a world of (learn/do) xyz quickly, so expectations need to be managed.

I'm enjoying your video thus far. Some constructive criticism: The summation code shown at 02:25 is wrong. It modifies the loop index variable, "i" and the "range(n)" function stops before 10, giving only 0-9. Your code has a value of "10" in "i" after the loop completes. However, if you change the variable "i" to "x" on lines 3 and 4 and increment the value passed to "range()", "i" ends up with the value "10". An equivalent one-liner: "print(sum(1 for i in range(n+1) if i != 4))". Although my advanced math is VERY rusty, I'm certain that answer is incorrect. You must add the value of the loop index variable with each iteration, NOT 1. That is: "print(sum(i for i in range(n+1) if i != 4))".

Thanks for the information brother

Amazing video. This video comes at the right time because as a college student, who is about to make an ML course in the next semester, I was a bit anxious as well. Thank you for the guide!

that last part of the video , is just so calming

@9:46. I agree that memorizing or coding opimtization is overkill. I wanted to learn everything by scratch and learnt that's a fool's errand. I learnt that the hard way. In deep learning, learning back propagation for FNNs, CNNs, RNNs are good ideas. But it is better to concentrate on the matrix algebra aspects of the forward prop than focus on the optimization part since most of the back prop is efficiently done in Tensorflow or PyTorch or for any ML algorithm in Scikit-learn. The mathematical derivation of any algorithm is more important than the cumbersome calculations. I took the Google's Tensorflow certification exam last year. What I learnt from the exam is the core idea of fitting the inputs and outputs in the correct matrix or numbers or any data augmentation or manipulation that better fits the model than raw data. To sum up, the practical aspects of the ML is all about how we frame the problem and how efficiently we solve the problem for real life use and debug our model when it goes crazy or throws up errors.

1. Math intuition 2.Foundation of math toolkits 3 learn how to debug 4 overview of the code base and foundation of code 5. As a PhD student now, I have to say it takes a lot of time and loads of stuff or even new things to keep up with everyday. Try to enjoy it

Needed this so much. I have been sitting and solving issues for the entire day. I am working at a startup.Was just so much frustrated. Can anyone suggest a good community where we can post our queries and get a bit sonner replies?

My man you and I think the same. Trust me I struggled with implementing a simple summation in my first code back in the days when I was doing my masters thesis. I was looking at a simple formula i.e. L1 = exp(2*pi*1575.42e6*time + doppler) and I literally had no idea to work implement this to generate a GPS signal in MATLAB and Python.

Brothers who stuck in this type of problem typically math related problems i suggest u to follow NPTL IITM deep learning course..

You are honest man, thank you🎉

This is what I needed to hear.

I am 12th class student i have decided to start machine learning after my exams and in my Bachelor degree... UKposts has recommend a great video to me...🎉

Wonderful video 🙏🏻🙏🏻🙏🏻🙏🏻 i am a phd student and i am struggling a lot. This video gave me some hope.

proofs are there so you understand the thinking behind the end result. It isn't there so you memorize it, its there so you understand it and can make the concept yours to be adapted for your use. Most concepts already are as generalized as can be but having it as a tool set rather than as a rule is the main idea.

Nice video! Machine Learning, Deep Learning, NLP are all challenging subfields of AI. I am in the process of taking them right now in graduate school.

Great video 👏

@Boris Meinardus : What is the tablet you are using at 4:47 ?

great content , I always struggled with the mathematics formulas , always procrastinated myself for not able to understand concepts , but I guess this video has changed my thinking a lot now..thankyou @boris

Great video! I am a college student and has already join some ML/DL courses. But I always think that the college resource cannot compare to some big companies like OpenAI, Google, Meta and so on. Lots of ML techniques have been mature by them. What could we do as just a college student? Thank you !

What is your opinion on fully remote ML engineering positions?, it is realistic for someone that lives for example, in Latam to look for a job at this field?

Hey boris I really like your advice on paper implementation but I am really confused on which paper to start implementing first. Could u please recommend papers which a beginner should focus on implementing

Good valid and applicable points: 1) Debugger as a self-teaching tool 2) work systematically 3) create a list of "math rules" to reflect your learning

hey @Boris Meinardus given your expertise in ML what papers should i read if i wanna build a product / a tool like say sora or any generative ai any advise would be much appreciated!

You are the boss. Can you share the math notes?

Hi 👋, great video! I’m a bachelor college student and I wanna start with research (as long term goal) in the field of ml. I learned the basics and did a few projects. As I said, my long-term goal is to provide truly new insights in this field. Do you have any advice to get there and maybe how to find a person that could help on my way? Are there any contact persons or institutions in German universities?

hey your suggestions are really useful unlike the roadmap of other creators thanks a lot for this. I am really confused that should i start learning about deep learning and neural networks or I should focus on the supervised learning algos like regression, classificatin and xgboost stuff that is used in data science as my goal is pursue carrier in data science ?

Bro you are making me get there.Everyday I spend 3+ hours coding and learning something new of machine learning.So far I can develop Linear Regression models,Logistic Regression and Decision Trees. I am a final year student for Bsc in Mathematical Sciences and multi-tasking.Soon going for an honors degree to advance.I do work on projects too,I hope that in the next 6+ months I will be here to tell you I got a job in machine learning,now its 02:00 (South Africa) and here I am on Yt for ml ,passion!!!

Hey! Your videos are really awesome to watch and helpful. Also, How to start reading research paper? Should I start from reading basic papers and increase the difficulty? Can you give more insights on this?

Sir if you can give books and resources exactly from where to learn maths and maths behind code , I am ready to give full attention . Thanks a lot !!

Brother,your video is really very helpful.In next video can you pls show some ml projects which can give some ideas about the ml.Also a confidence to build one

Best advise is actually the last one. I hate how the first few are not so useful because it's already obvious that the key to understanding machine learning is understanding the math and the implementation. could use some discussion about the best beginning topics such as understanding of some functions that are widely used

can you make a video on foreign AI/ML opportunities as well for beginners who has stepped into this field and up-skilling themselves right now, share some resources and platforms to apply

this video is Gold

Would you recommend doing leetcode-type questions if the end goal is to become a machine learning engineer?

The head in hands shot was the truth lol

Can you give source of math that supports your learning like book or website that you see it easy to understand and how you can find it please

How can Impliment Hybrid model on ResNet5o and any other classify(e.g RandomForest, or SVM etc), any Resources will be Helpful, thank you in advance

I see a video of Boris, I click!

Love your videos bro

A test is a proof of concept to prepare you for the practise as derived from the concept

Like how many hours or days did it take you to build on large code base

Nice one ❤❤❤❤❤❤❤❤❤

I am middle schooler with IGCSE level knowledge. Is it possible for me to learn machhine learning online just by myself??

0:29 Nice to finally see someone thinking the same things,the same way. But there's a caveat I guess, you can't think of a black hole but math does exist, same for quantum mechanics. But I guess it could also be the case that these are not best approximation of the existing phenomenon and therefore are not intuitive.

Does the tip on studying just the 2 py files (train , eval).. work on the comma ai codebase? I'm also very interested in what automation tools they use to regression test each release. Did they setup and build custom tools?

Thanks

I am just in high school, trying to learn ml by myself , got stuck in linear regression, i get how it works , but cant progress

Random question, but what is your note taking app you use?

Boris I believe in you that one day you will become the next Ilya Sutskever.

How to find a unique project as already there are so many same projects on internet

what an amazing video man

Software Alert 2024: Universal Software Model -- The future in the software world! 100 programming languages? Why not only one? I built usmXX as an Operating System for managing any informational problem. The system is based on only three concepts: Parameters of the problems, Subjects (not objects!) and the Informational Individuals!

Great video! I'm new here but your videos do seem sort of AI modified, like with Nvidia's maxine. Is their some sort of truth to that? Genuinely curious 😅

Where do you work Boris

Math steps just have rules passes one step of a time 3:45

Bro how can i learn ml with my collage its very difficult and from where to start pls reply

Tu berlin or humboldt university of berlin for computer science and entrepreneurship

Can i start coding on just a smartphone? I am from third world country and am tight on budget to buy a decent laptop.

The code at 2:24 is very wrong and bad at the same time A better version would be : ----------------------- n = 10 for i in range(n): if i == 4: continue

I am a full stack developer and I am learning ML/Data Science for last 4 months, I have came across NLP, Probability ,Statistics, Linear Algebra and Calculus, and my coding skills already great. This video is again making me to get confused although I know how things work here. Sir, Your video should be more focused on how ML/Data science concepts can solve the real world problems or can help business or how you can make money through it. Then things would be much clearer. Currently I have less knowledge of it. when I will get the knowledge of it. I will Definity share it. But any beginner/ Intermediate should avoid this video. Focus on how to solve real world problems using ML/Data science concepts and eventually look for different resources on UKposts and eventually things would be clear. Remember. The beginning is always the hardest part to overcome

ML Engineers are not mathematicians. As long as you are not working in academia and research fields or building something new from scratch (very rare!), you are not required to derive or fully understand complex mathematical formulae. Much important is to know how and where to use those formulae and having general idea what’s going on.

is that Oby in your video 😅😅?

Yup

Great video Please make some videos on how to get ML/DS job for freshers

Wish someone explained this to me in university

4:06 ,Secret 6: Everyone was scared, confused and annoyed. You are not alone, get used to it.

W bro

Albert Einstein said "If you are out to describe the truth, leave elegance to the tailor." I think he was saying the following. Learn the truth that inspired the formula.

Don’t sleep on learning more about discrete math and stats !

😄😄 colleges, schools are good at not teaching math the way it should be taught, which why its so difficult to know when to use summation Vs when to use integration 😆. Mathematics is most essential tool needed to learn and understand AI

With almost 3 decades of programming experience I can tell you that programming is indeed debugging! Spot on that comment!

I am officially deciding to say Maath from now on.

Look at math Backwards 0:47 math is formalized Human observations

"poor X skills are the number one reason for failure in adopting X". how much money they pour on such a study, I wonder

it's practice

Debugging is coding

I saw a tweet yesterday it says "The more you write code the more you realize that farming would have been a better option"😂😂

13 mins of 10k Hours has been used😅

10 000 hour rule was debunked already ;)

All bull shit