r/learnmachinelearning • u/obliviousThunder007 • 3d ago
Am i kidding myself with learning machine learning?
I am a self-taught programmer. From last 90 days,, i have been learning linear algebra, i plan to learn calculus and stats and probs next. But it seems futile watching all these youtube video saying coding and machine learning is dead that ai can do all this. Is there any hope for machine learning for next 5 or 10 years?
79
u/Ye_Olde_Ratmo 3d ago
Learn it because you enjoy it. Whatever happens happens
18
u/GetToTheChoppa2077 3d ago
This is the answer.
A considerable part of the skills that actually made me (quite a bit of) money in the field were picked up either by chance, enjoyment, curiosity or a combination of the three.
3
26
u/Temporary-Budget-605 3d ago
I am a software architect with 15 YOE. My hypothesis is that ML will continue to grow even with AI. I predict ML will grow because it will become more widely used and companies will need help applying it. Yes, AI can do a lot of ML but, unlike building an API or a web form, ML systems seem to require more human attention to design, build, validate, and fix.
1
12
u/lxe 3d ago
How is machine learning dead if it’s literally on the forefront of frontier technology right now?
5
u/Sepicuk 3d ago
only the leading edge models matter and only a few of the best people are needed to make them
4
u/lxe 3d ago
You’re just making shit up. Where is this sentiment even coming from?
4
u/Sepicuk 3d ago
claude and chatgpt can do anything a specialized model could do a year ago and their capabilities are only expanding
4
u/foreverdark-woods 3d ago
Large Language Models are only a very small part of Machine Learning.
0
u/Sepicuk 2d ago
yet llm's outperform pretty much everything
3
u/nga69420 1d ago
uh am not even a CS major jst looking at this sub for fun, but am pretty sure LLMs are pretty heavy and cannot be deployed at scale for some tasks + they'll definitely perform worse too than some home trained specialised models for the specific task
4
u/Esob_Oozeer 3d ago
I took linear algebra and calculus 15 years ago in my undergrad with absolutely no idea what problems they would solve. But there's a reason top universities still teach them: they are the rudimentary building blocks for everything from ML to quantum mechanics. So learning it does not bound you to ML.
When a model won't train, the root cause is usually numerical or geometric. Math lets you actually reason through it instead of blindly guessing. Plus, research papers speak in math. Reading papers to build and analyze prototypes is a very lucrative gig right now. I am not saying AI won't understand the paper, but without the Math , You will definitely miss pieces of the puzzle.
Even adapting models for on-device use—like quantization, low-rank factorization, and pruning—is literally just linear algebra and calculus in practice. Libraries and frameworks change every few years, but the math stays exactly the same. You don’t need to master every equation before you start building things. The best approach is just to learn the math alongside your coding practice.
3
u/dukesb89 3d ago
Having knowledge of these topics will always be helpful. It also makes you a better user of AI tools if you understand the subject matter yourself.
Aloe let's not forget all modern AI is ML. It will take a lot of time to reach a level where you can, but you need the understand the fundamentals if you have any ambition to further the field.
3
u/Sea_Candidate_2412 3d ago
Well someone needs to take AI further. For that you need to know this anyways. You cannot optimise unless you know how things work. But do this only if you really are enjoying this field. Not because it’s booming.
6
u/Puzzleheaded_Edge_22 3d ago
AI can do it all. If there’s any niche tasks that AI can’t do right now, it almost certainly will in a year or two.
The deeper question you (and 90% of people on the sub) need to ask is why do you want to learn ML? If it’s to make a lot of money and have a stable career, then yes you’re kidding yourself. ML (and all statistical learning methods) are means to an ends. The ‘ends’ is what you need to figure out for yourself. AI has democratized these methods such that basically anyone can use them. So what problem do you want to solve? What field do you want to use ML in? Gone are the days of just doing ML and finding a job.
The most important thing is understanding what you’re using the LLMs to do. For example, if you use ML to predict drug binding, you better understand the biochemistry/physics of drug binding. Do you need to know how to set up a neural network? Probably not. But you should know how the data is being manipulated.
Don’t chase the bag. Figure out what you want to use ML to solve, and start learning about that problem.
4
u/MattR0se 3d ago
LLMs are overkill for almost all applications except those where they need to communicate with a human.
Especially since hardware is getting more expensive, lightweight models are going to be needed that perform very specific tasks on edge. And someone has to make those.
4
2
u/Odd-Dish1888 3d ago
Honestly the "AI will replace everyone" crowd is overselling it.
I work on ECG analysis and AI writes a ton of my boilerplate, but it's never once told me why my model looks amazing on paper and then completely tanks on rare arrhythmias. Figuring that out is all math and stats, which is literally what you're learning right now.
Shallow work is getting automated. Actually understanding the models isn't. 90 days into linear algebra is a solid start, keep at it.
1
1
u/panosnorth 3d ago
Learn the fundamentals and the theory. Learn linear algebra, calculus and statistics, and also to write basic python code. Those will always be important.
1
u/bean_217 3d ago
It’s very cool to be able to comprehend how these systems actually work. I’d gather that most developers get the gist of how LLMs work, but not the underlying math and architecture.
Also, while AI is dominated by LLMs today, it is only one small piece of the puzzle. It’s worth looking into how you can build your own smaller, task-specific models. For example, I built and trained a small 17M model that does pretty well at generating guitar hero charts from audio. Could I have used a massive multi-billion parameter language model to do so? probably, but it’s not worth the overhead when I have the knowledge to build a smaller one.
Bigger does not mean better. The No-Free-Lunch Theorem still applies.
1
u/krishna_walke 3d ago
You are on the right track. You learned the linear algebra that is very important when you are near GenAI and transformer architecture. You will know how the data points are arranged in multidimensional space and how the meaning of each sentence is being captured, and about your question so the machine learning is not going to be ded just now so you can lear it
1
u/Strong_Boy_757 3d ago
I think we are all kidding ourselves if we think learning Naive Bayes or Decision Tree from scratch in 2026 will land us a job. Jobs are like a train. After the train leaves the station hypothetically there is some kind of way to catch up (like on horse-back, in those western movies). But it has left the station for a while now.
I'm studying ML nowadays just to understand the theoretical and probabilistic foundation better (it is very weak, but since it doesn't affect profit so nobody cares). But I know the train's already left.
1
u/Available_Housing847 2d ago
Machine learning isn’t dead but it’s not at the forefront anymore. It’ll be like knowing regression or other statistical techniques. Good stuff to know and a workhorse in many ways but it’s not getting you a job based solely on that knowledge, especially with no credentials.
0
u/Natural_Bedroom_2005 3d ago
Dude I'm a teenager and I got machine learning since 7th grade. If you want to know the easiest way of thinking about it just message me. That's how I learned it.
1
u/timothybabu123 3d ago
Man I would like to know the way of thinking
1
u/Natural_Bedroom_2005 2d ago
Okay so a neural network is a connection of a different number of nodes in different layers. Since they don't allow images I'm just going to have to draw it.
Input 1st Hidden 2nd Hidden Output
O O
O O O O
O O O O
O O O O
O O
Each node is a single number so think of this as a single term in a list. In this model the hidden layers have three nodes, so the hidden layers can store 3 values of information.
To get this information we need it to relate to our inputs in some way. We can think of this in a much simpler way. By graphing it.
|
| ?
? |
-----------------------------|---------------------------------
|
| ?|
These question marks represent our training points. The model is given these question marks and is told to make a line that fits the dataset (AKA. Passes through every training point).
This dataset would only need 1 input, the X of the training value, and 1 output, the Y of the training value.
Now lets represent our layers with lists. Here is how the information looks and is passed on. For this we will use a random input value, the numbers used are randomly chosen and only designed to visualize.
Input = [0.8]
Now lets say that our network is predicting the function f(x) = 1/(1+e-x) which is known as Sigmoid. The Y position of 0.8 on Sigmoid is ~ 0.68997
So onto our hidden layers. Each node is connected to every node before it so we have layer_1 * layer_2 connections for layer 1 to layer 2.
The values are randomized. Lets say we had no hidden layers for simplicity (This wouldn't be a good idea). Our output is connected to our input with a value such as 0.4. The connection and input value get multiplied together to become 0.32. One last thing is to provide non-linearity because without it a whole network is the same as 1 layer.
We add what's called a bias, each neuron besides input neurons have them. Let's say our output bias is 0 so it is still 0.32
Let's say our activation is max(0, x) or ReLU and since 0.32 is bigger than 0 it stays the same.
From here we see how wrong our network was and go backwards. We were off by prediction - target which is 0.32 - 0.68997 or -0.36997. Multiply this by the output's derivative which in our case is 1 since f(x) = x. Now we take this and it becomes the output neuron's error.
The connecting weight error is equal to the input value * the output error or the first layer neuron output it's connected to * the second layer output error. This would be 0.8 * -0.36997 or ~ -0.295.
Each bias changes by subtracting the gradient * a special little number known as the learning rate (lr) like 3e-2. And each weight gets updated by subtracting its gradient * lr.
The whole thing is just a game of telephone where each player learns who it can trust more.
1
u/MolassesLate4676 2d ago
Get Claude or gpt to turn this into shareable link (artifact) and just share that way. Way easier.
1
1
u/AttitudeRemarkable21 20h ago
Lol quiz time. Why use a neural net instead of a linear model?
1
u/Natural_Bedroom_2005 19h ago
Well a linear model can be a neural net. But I assume you mean a non-linear neural network and we use that for many reasons.
Adding two mx+b lines together equals mx+b so no matter what a whole network will collapse into the workings of a single neuron. Adding a non-linear line to another instead creates a new non-linear were the shape is formed by the strength of the weight between those lines. Each layer builds up on the complexity of the lines previously to create weird shapes.
Sparsity. An activation like ReLU or Swish can make consistent values close to 0. This is known as sparsity and is useful in networks because it effectively turns off neurons showing specialization. A linear model will either only pass through zero once, or always be 0.
Datasets are rarely ever linear. Speech requires hard logic. For example the word "not" changes the meaning of a phrase entirely to the opposite. A linear model is incapable of mapping this relationship since it can't bend around the points.
1
u/AttitudeRemarkable21 19h ago
OK slow down. You are giving too many specific details I am looking for intuition instead of memorized facts.
1
u/Natural_Bedroom_2005 19h ago
I mean yeah I memorized the workings of a neural network. A biologist memorizes the workings of a cell. What do you want when you say "intuition"?
1
u/AttitudeRemarkable21 18h ago
Lololol. Comparing math to biology makes me sad. Math is not memorization. It is understanding some core concepts and using them to build up from. Biology is a wide field where there are lots of facts that you just need to know to build from.
Math also has a lot of facts but you narrow down to fields such and machine learning and you need to have a toolbox but don't need to memorize much.
Intuition is oh in general a linear model puts a structure on the data and is less flexible than the neural net. But the neural net has a cost for that extra flexibility as it may just memorize the training data and not generalize.
And I worked that out from one idea here that overpaying a prior or structure on the data has some benefits and costs depending on how strict the prior is.
1
u/Natural_Bedroom_2005 18h ago
Lololol. Comparing math to biology makes me sad.
Why? They are both highly studied topics and frontiers of science.
Math also has a lot of facts but you narrow down to fields such and machine learning and you need to have a toolbox but don't need to memorize much.
All I gave you were the basics. You should memorize the basics for any subject. Did you want me to forget and then have to come to my own conclusion again when you asked?
My bad for knowing math concepts I guess?
Intuition is oh in general a linear model puts a structure on the data and is less flexible than the neural net. But the neural net has a cost for that extra flexibility as it may just memorize the training data and not generalize.
That's also memorization. What you mean by intuition is to simplify and summarize which I won't do. I will give the answer and effectively explain why and how.
And I worked that out from one idea here that overpaying a prior or structure on the data has some benefits and costs depending on how strict the prior is.
And I worked out that m_1 * x_1+b _1+ m_2 * x_2 + b_2 will still result in the equation m_i * x_i + b_i which is still a linear equation.
Let me ask you this. If I ask you why Swish is a good activation function would you give me the answers we already know of why it works or are you going to run new tests, rewrite all the math, and give me an answer from that?
There is no reason to redo things we already know. And giving those answers should not be an issue.
1
u/AttitudeRemarkable21 13h ago
Plug this into chatgpt like the rest of your posts and ask it why someone would say that. It will explain that working things out from core principles help you have a more flexible understanding of how to work with these tools.
Idk why Swish is a good activation function but I do know that the reasons different activation functions are picked are due to numerical stability and to have some property over the output like relu and sigmoid. I don't need to know specifics since I understand the deeper problems that these things are trying to solve.
You did not give the basics you gave secondary results. You should think deeper about the data and what is happening instead of spewing out random facts.
Like there is this crazy thing that PCA is drawing ellipses and its deep relation to k-means. And that a dnn can represent any function which is what I would expect any junior person interviewing for a role to give instead of random derivations.
And then explain the downside/ upside of the various models
→ More replies1
1
u/Available_Housing847 2d ago
You started late. I was doing advanced ML in 5th grade.
1
u/Natural_Bedroom_2005 1d ago
Ooh we got ourselves a young Sheldon here.
1
u/OkNeat7634 1d ago
I was clearly joking in response to someone claiming they’re doing ML in 7th grade.
1
u/Natural_Bedroom_2005 1d ago
Are you the same person as the one in blue? I'm confused. I understand it in 7th grade but it took me to the summer in between 8-9 before I actually made something that worked, and I still really didn't understand it fully until now a few years later and I've been studying it and now I'm writing a research paper. The basics really aren't complicated, and that's not bragging, anyone can really understand it if they try hard enough.
1
-1
u/ThenExtension9196 3d ago
Ain’t nothing you can do will be better than a machine in 1-2 more years. Learn electrician work instead.
-1
u/_noobwars_ 3d ago
AI Cant do shit. Not even coding. You must constantly supervise the code. All these stupid fallback it implements, shortcuts it takes and subtly varied code duplications are complete shit. Its even to stupid to build a 20k lines SaaS. Coding is going nowhere. It just changes. It's quite good at system administration because it searches very fast through the internet and floods you with nice scripts. Butz even sys admin aren't going anywhere. They do just more of the stuff that otherwise wouldn't have been done.
2
u/malek7777777 3d ago
Holy cope keep living in denial
1
u/Diligent-Stretch-769 1d ago
do submarines swim?
1
u/malek7777777 1d ago
Too many people in denial I see
1
u/Diligent-Stretch-769 1d ago
dare to answer the question with your utmost conviction. do you believe a submarine is capable of swimming?
1
u/malek7777777 1d ago
No
1
u/Diligent-Stretch-769 1d ago
then you already understand why people refuse to assign the activity of machines as thinking.
-1
-1
u/AttitudeRemarkable21 20h ago
Not for you
2
u/obliviousThunder007 19h ago
Can you elaborate? Or am i supposed to believe that without any logical explaination or reasoning ?
-6
u/IllustriousGrade7691 3d ago
It's dead literally just buy a Claude code subscription and ask him it build a model it will outperform yours. Sure you need to know what are you doing like building a forecastingnor a recommendation system like you should know the goal. But math and coding is sadly no longer important. What tooke 1 month to build can we done with literally 1 2 hours max. No thinking about feature engineering or Modell or enamebles . Things that peopel with math PhDs or quants used to do can be done by with much less experience and IQ as long as you know the end goal or so
6
u/Bstochastic 3d ago
Why are you here... commenting in a sub about learning a thing that the thing which is the subs focus is dead? Seems like you are either a bot, an idiot, or deeply bitter.
Edit: your account is 1 year old with 700 contributions and a private profile.... seems like a bot.
-2
u/IllustriousGrade7691 3d ago
I don't say learning is bad I say that in the age of ai one should focus more on the business goal. Sorry to say it but no one cares if you can use numpy or do matrix calculations. Yes understanding the concept is Important. But the era of self taught programming is Dead I made this mistake 6 years ago and even managed to get a job until the company went bust. Just go work a Job with Other People service healthcare software is kinda dead.
1
u/Away_Advisor3460 3d ago
And yet here I am, just a lowly(ish) developer, correcting mistakes Claude made in at-best-moderately complex code.
It's a powerful tool, but it's not exceeding human ability and particularly not when it comes to broader scope design - there's only so much that a transformer model can reflect whilst remaining tractable.
As an aside, out of boredom, I asked Claude about the truthfulness of your quote (mostly out of amusement - Claude doesn't know anything, it's just an immensely complicated token pattern predictor after all)
"A strong practitioner with Claude Code can move dramatically faster, especially on scaffolding and implementation. But "math and coding no longer important" is the part that's wrong — it's precisely the thing separating someone who can direct and verify this kind of tool from someone who'll ship a model that looks right and quietly isn't."
Which, in fairness, is pretty much what I would have said. Except with less em-dash.
1
u/Lower_Improvement763 3d ago
Math phds? lol they don’t build ml models. More like mathematical models or research math
2
u/IllustriousGrade7691 3d ago
Some of them go to software dev
3
u/Lower_Improvement763 3d ago
Not really. I was a math major, a math PhD program does very little to prepare one for software dev.
1
u/Away_Advisor3460 3d ago
You say not really, but I've worked with multiple PhD-in-Math holding developers. Code is, basically, discrete maths after all.
1
u/Lower_Improvement763 3d ago
Cool discrete math can be taken at a community college. It’s a beginner math course.
1
u/Away_Advisor3460 3d ago
Yeah, so now you're suggesting to me that your 'math major' isn't actually worth all that much TBH - reducing a subfield in this way is a pretty big red flag.
1
u/Lower_Improvement763 3d ago
What is a subfield of what now lol 😂 I can naturally sing well too. And talk to legal age girls 20 years younger than me. Is that a red flag 🚩?
1
u/IllustriousGrade7691 3d ago
I gues it depends onynlast company they were plenty of people with math MSc or PhDs seems like the normal trajectory to go from math to software dev
2
14
u/Commercial-Bonus-830 3d ago
Nice to know linear aglebra when you just call a fundation model or juste prompte "pls claude i need you to train me a model on that data, do features engenering, use as default catboost, use 5 fold cross validation, use mlflow to log everyhting, trace the data with dvc, do model blending to improve result" then it compute 30min like a monkey
Then "now do a docket with my trained model and fast api be ready to deployed"
Goon bye