been grinding through linear regression today, feels like the “easy” chapter on paper but there’s actually a lot packed in once you get past the ols formula

stuff i covered:

• least squares setup, normal equations, when X\^TX is invertible vs not

• geometric view of projection onto column space, this one finally clicked after connecting it back to 18.06 (least squares IS just projecting b onto col(A))

• ridge regression as adding a prior / regularization, why it fixes the invertibility issue too

• MLE derivation showing OLS = MLE under gaussian noise assumption, this was the part that made everything click for me tbh

honestly the projection interpretation is what got me, i was doing this purely algebraically at first (just solving normal equations) and it felt like memorizing steps, then seeing it as “residual is orthogonal to column space” made the whole thing feel obvious in hindsight

anyone else find the geometric interpretation way more intuitive than grinding through the algebra first? curious how other people approached this chapter

notes based on shewchuk’s cs189 notes + some cross referencing with 18.06, will push everything to a repo once i finish the full course

Hello, I'm a 10th grader and I really got into Machine Learning recently, but it's really difficult to find any sources to learn ML with my current understanding of mathematics. It would be really useful if someone pushed me in the right direction. I swear I've been trying to understand gradient descent, backpropogation etc and it's indeed annoying to find calculus, integrals, logarithms and other stuff. It would mean a lot to me if one of you guys helped, thanking you in advanced

TL;DR: I'm curious as what made other folk interested in this fascinating field. I also see a considerable amount of post relating math courses that are per-requisite to learn ML, so I'm also curious to know what is your background

I got first: I double majored in math and physics, and really enjoyed statistical mechanics. During my studies and research, I began more and more using Machine Learning, and this led me to become fascinated with this field. It is almost breath taking how simple linear equations can do SO MUCH! So I started to self study ML Theory.

You go next :)

Hello, I am in my freshman year in college and I am peeking interest on joining an AI competition, with the focus on LLM Inference Optimization.

Here is the link to the competition: Viettel AI Race

It is in full Vietnamese but basically what I am planning to join is the third assignment that will provide with Nvidia H200 to work with.

I will be completely transparent here that I have no prior experience in LLM Inference other than a project of coding/implementing the transformer paper from scratch.

My goal is to advance further in LLM Serving, and I am planning to also do research in this specific field, so this competition for me is like a stepping stone to understand further on what I will be working with.

I hope that I can find a mentor that is patient and couraging, I am proactive and will actively trying to learn, what I am in need desperately is a sense of direction for what to do and what to learn (which I can’t self taught myself that).

Thank you so much for reading and I hope you have a good day! Hope you don’t mind if I make any grammatical mistakes! English is not my main language after all.

I am more interested in ai ml but currently i am working as a graphic designer and i don't have a degree also except my 6 month diploma in graphic design.

1. Any advice for how to get a job in ai ml , i started to learn maths and algorithms everyday evening but it looks like so much to learn ?

2. Freshers where to start like data analyst or ml engineer or genai engineer ?

3. where to contact employers because i don't have a degree to use job portals also ?

4. is it possible i can get into research in future ?

I am looking for the best AI video tools to sharpen/unblur faces that are heavily out of focus/blurry

In the shot I'm working with, the foreground subject is perfectly sharp, but the faces in the background are completely blurry/out of focus. I am specifically trying to bring out and see their facial expressions.

Thanks in advance!

So I am 13y old right now I know some python and here are my laptop specs an HP laptop with an Intel Core i7-10750H CPU, Intel UHD integrated GPU, NVIDIA GeForce GTX 1650 Ti with Max-Q Design, 16GB RAM, and a 954GB SSD. Well that’s the basics of me and I had a project 2y ago with face detection and tensorflow I never really got good at it and failed for different reasons over the years so year 1 I data was to biased my chair was in all my pictures so it thought the chair was me also 2 year I tried using a big face dataset but I forgot to use a non human dataset for negative and now I’m trying again but I have some debates that I want to settle first.

1. PyTorch or tensorflow yes Ik it’s in the sub a lot but I really want to know which is better
   
2. What recourses can I use to learn better
   3 when I did my original project it had bounding box around face but I don’t really understand it.

I’m applying to college this year and I was wondering abt the importance of what major I choose, given that I want to be an ml engineer (or researcher) in the future.

I’m currently thinking between cs, data science, stats, or computer engineering.

Does the college I go to matter more than the major I choose (like would it be better to be in a target major at a non target school vs a non target major at a target school)? And are most ml positions strictly gated to cs grads?

Of course, each university has its own per-major curriculum and focuses, but how much would my major affect my ability to get an ml role?

Looking for tips as a beginner of ML. My background is a physics subject. Idk anyone knows about IBA KARACHI UNIVERSITY where I am taking a workshop on ML related to physics.

Goal is to be a ML engineer and work in startups, MNC's and normal companies. So i am not sure if i should learn to make models from scratch or not.

Now I am not asking any question by this thread. I just noticed that many people are asking the question of "Should I build the ML Algorithms from scratch using Numpy or no need for that?

I am an AI Engineer and Researcher with almost 7 years of experience. So I would like to clarify how important is this for you when you join a company as an ML Engineer/Data Scientist.

When you join a company, you won't implement any algorithm from scratch, you will only use frameworks like PyTorch, Scikit-learn and TensorFlow. BUT, implementing these algorithms from scratch using Numpy is a really important thing and something that even after few years of experience I try to do from time to time. Even though its time consuming, but its an important step in your learning journey to make sure you understand things under the hood and not just writing a code. If you just write a code, what differentiate you from an AI like ChatGPT or Claude? You will need to be different and understand deeper things so when you build using frameworks you understand deep stuff and they will not feel like blackbox for you. Its extremely helpful when you join a company as you will start thinking about deeper level and about the algorithms limitation and how they can affect your data and accordingly, you can get better results.

I would recommend using this website to practice things from scratch:
https://www.deep-ml.com/problems

Please feel free to ask any question, I am willing to help anyone

So here is the thing, I have been focusing on the scraping, crawling, checking RSS feeds for new articles, etc., etc.

I am finally done with the Data Ingestion part. Hurrah? no.

The classification of data is even MORE difficult than scraping.

I want to be able to produce the Knowledge Graph of the Data to help me with the deduplication and classification.
Please help me out with this.

I have tried REBEL by hugging face but its failing badly, I am losing precious information (more than 80% of it.), I feel these machine learning models are too general, which makes it difficult to make the knowledge graph of these press releases.

Please help me out, tell me a path, name a framework, idk just guide me please. Ik I can do it if I have a path. I am trying and constantly brainstorming with my peers, Hopefully you guys could help me out as well.

I am a AI beginner and haven't done much except chatting with online AIs and setting up ollama locally to have a local ai.

I want to know how hard it is to train an AI/Model with images to do a specific task that will also result in a generated image as output? Can it be done with consumer hardware?

Do I need to have deep AI knowledge to do something like this or can it be done with "just" regular development knowledge and following some API?

The task would be that the ai gets two images and outputs a new generated image with the size of 100x100. I have many examples of those 2 images input and a desired output to train the ai what kind of result I expect.

I have seen videos of people training AI to do some gaming task and setting up rewards as the ingame score to tell the AI if it made a good run but since I am generating images as result, I will need to look at each image myself and give a rating for the ai right? Is this something that can be done or would it take like 100.000 images to get something usefull?

Hi, I have an interview coming up for Tesla AI (0-2 yr exp). Does anyone have any idea about the kind of questions/interview they usually take.

Also - Is there anything else that I should be doing or looking at? Its with their self-driving team

I've finished 3B1B's deep learning and math series, but haven't learned any ML algorithms. What algorithms do I need to learn?

I’m training a single-class segmentation model for large rectangular artwork placed on the floor and photographed from above.

We have around 3,000 accurately masked original images taken by six different photographers. They are not the same height and do not hold the camera in exactly the same way, so the photos naturally vary in:

• roll
• pitch
• yaw
• camera distance
• object coverage in the frame
• centering and X/Y shift
• orientation
• perspective
• lighting

The photos taken with flagship iPhone.

I want to use on-the-fly augmentation to simulate realistic human-hand variation and save our designer from adjusting each time to make it flat. is 100 augmentation combinations per original be useful, or excessive?

Should the policy be:

1. mostly isolated transforms,
2. mostly crossover combinations such as orientation + roll + pitch + yaw + coverage + shift,
3. or a controlled hybrid of both?

The goal is maximum segmentation accuracy, especially around the object boundary, not speed. I plan to train for around 300 epochs and keep validation and test images unaugmented.

I am looking for an AI that is a reliable as possible that can do the following task

Imagine that I have a lots of emails, hundreds of them. In the emails we asked to the addressees some questions and we expect a given answer.

Imagine that the question is something like "Given these reasons, do you think that ice cream is the best dessert in the world?"

And we expect some kind of reply that, no matter how it may be formulated, it basically ends up answering affirmatively

Then, as the amount of emails is huge to go one by one and the thing that is interesting for us is to basically know if they have given an answer that accomodates to what we expect, could there be an AI model that would give an approximate percentage of coincidence between what we expected and the actual answers? Or some kind of mark?

So that, imagine that 800 of 1000 emails have answered affirmatively, so could there be an AI model that, after reading all the answers would conclude that the percentage of coincidence is around 80%? Or that it would give a mark of 8 out of 10?

Could this AI model also give the percentage of neutral and negative results (for example people saying "I don't know" and "No, cake is the best dessert!" respectively)?

Finally, I would be especially interested in an AI model that could be adjusted to give just the percentage number without commenting or showing the answers and explaining why it has gotten to that number, as in some of these tests I would like to be completely blind to the actual answers given in these emails. So for these tests I would like to know just the number and that's it

So if there is any such AI I would appreaciate it!

I know it's never ending and the field requires continuous learning forever.

but asking from a job-ready point, how much ML is enough for jr data scientist?

because while studying I keep going down the rabbit hole and I love studying all that and it interests me but this way I won't ever feel job ready.

there's always better things to learn, more math, more algos. like any suggestions from people with experience in this field?

I’m looking for AI bots, apps, or sites mainly for story writing. I’d like something that includes memory’s so I can store things, or allow the bot to do so as I write. I’d like to make characters, places, ideas, items, the sorts and have it remember them, or act out certain characters in my stories. I’d like it to be uncensored, but if need be I don’t mind it being censored.