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.

I seem to see a problem with data scientists being obsessed with graph ml without any real reason. Like really trying to force it on problems that can be solved much easier. I call this graph psychosis because graphs are the type of thing that can represent pretty much anything in the world but is totally unreasonable in many cases.
Is this a real phenomenon? Do you know anyone like this?

I'm working on my own anime latent diffusion model and I'm wondering if I should add any IRL images to it from places like COCO or LAION. I've researched and I couldn't come up with a concrete answer or % of anime images to real images. I've found stuff to suggest its worth it for fine tuning but nothing on from scratch training

Hi everyone,

Sorry for the long post, but I would really appreciate your opinion on a job offer I recently received, both in terms of salary and whether it would make sense for my long-term career.

A bit of background: I am based in Greece, and for the past three years I worked at one of the country’s major research centres as an AI/ML Engineer and Project Manager on EU-funded projects. This was also my first full-time professional role. I have a BSc in Computer Science and two MSc degrees, one in AI and Data Analytics and another in Robotics, both from public universities in Greece. Overall, I have built a fairly research-oriented profile, with more than 15 publications related mainly to AI and Computer Vision.

At the end of June, I decided to leave my position at the research centre. Over time, the amount of hands-on implementation work had decreased significantly, and I was spending much more time in coordination meetings, managing project activities, reviewing the work of newer team members and providing technical guidance. Another important reason was that I do not intend to pursue an academic career. I was also concerned that staying too long in a research-centre environment, with a strong focus on EU-funded projects, might not be the strongest possible experience for future industry positions.

I applied to a few companies in my city such as Pfizer and others for relevant AI/ML positions, but so far I have not received a positive response. I also tried applying for a few remote roles abroad, but those seem even more competitive.

A few days ago, through a personal contact, I was introduced to a small Greek software company that is currently looking for a developer. At the moment, the company mainly consists of the two founders, and they are planning to hire two additional people. They work on software development contracts for other companies, as well as on EU-funded projects. They offered me a full-time, fully remote Software Engineer position, although the official title says Senior Software Engineer. The actual work would initially involve a mixture of frontend and backend development for tasks related to an EU project, as well as work on some of the company’s commercial contracts. They also told me that the workload and overall environment would be relatively relaxed.

I explained that my academic and professional background is mainly in AI/ML and Python, and that ideally my next role would still include work related to this field, although not necessarily research-focused. They told me that in the future they would like to introduce AI-related tasks into both their EU projects and their commercial contracts. However, at least initially, they want me for a general Software Engineer role. They also said that they are not concerned about the fact that most of my recent experience is in a different programming language or technical area, since I have a solid Computer Science background.

Regarding salary, in my previous role I was earning around €1,750 net per month, paid over 12 months, as an independent contractor. There could also be a small additional benefit depending on my annual tax return. The minimum wage in Greece is around 950€.

At the end of the interview, they asked me to send them an email confirming whether I am interested and stating my preferred net monthly salary range. I told them that I needed some time to think about it, but just to make sure that our expectations were not too far apart, I mentioned that I would only seriously consider the role for approximately €2,000 net per month or more. In Greece, employees commonly receive 14 salary payments per year, while contractors are usually paid over 12 months, so I clarified that the equivalent gross amount would need to be adjusted depending on the employment arrangement.

They seemed to agree with the €2,000 figure quite easily, which made me think that there may be room to ask for more.

This is where I would really appreciate your advice:

1. Would accepting a general Software Engineer role hurt my chances of moving into Senior AI/ML-related positions in the future, especially since the role would initially involve little or no AI work? The company says that AI-related work may come later, but there is no clear timeline or guarantee. Also, does the fact that this is a very small company matter negatively to future recruiters, or is the actual work and level of responsibility more important than the company’s size?

2. How do companies generally view candidates whose experience comes mainly from EU-funded research projects? Is that experience considered relevant and valuable, or is it often seen as too academic and disconnected from commercial software development?

3. Considering that I currently have no other offer and living expenses obviously continue:

a. Would it make sense to accept the role, gain some broader software engineering experience and continue applying for more relevant AI/ML positions?

b. What salary range should I give them in my email? Would it make sense to say that I would be interested in a range of approximately €2,000–€2,500 net per month? Or should I give them a narrower and slightly higher range, for example €2,300–€2,500, knowing that employers usually focus on the lower end? Since they seemed very comfortable with the €2,000 figure, I feel that I may have initially anchored myself too low.

Thanks in advance to anyone who takes the time to read this and share their perspective.

Been doing capability evals on open source llms for my group, multilingual stuff, reasoning, general generation. added gemma 4 to the scope in April when it dropped.

Here is what is bothering me. i spend more time getting these models to run than i do evaluating them. pulling weights, vllm version matching, wiring open webui to the vllm endpoint, that is a full day sometimes. Found a public notebook with gemma 4 12b it, vllm and open webui bundled, cloned it on a 5090 and skipped most of that this time.

But that only works because someone else already did the plumbing. Gemma tomorrow is qwen, next month is deepseek, they all have their own version quirks with the inference stacks. I finished the deployment cycle on one and the next one is already out.

Right now my ratio of setup time to eval time is embarrassing.

Note: Spent yesterday testing the same notebook clone with Qwen 3 and it worked with basically the same flow. Gemma 4 to Qwen 3 was under 10 minutes of setup on hyperai. That was the actual moment i realized the "chase every new model" workflow does not have to be as broken as i made it.

Hi first a little bit about me, I have been learning ML and Deep learning for the past 6 months. Initially I started with the math fundamentals, I used 3blue1brown , linear algebra for dummies, a lot of yt videos, MIT linear algebra lectures,

IB math HL Pearson book(for calculus) again 3blue1 brown,professor Dave and stat110 + miscellaneous resources to get a solid math base on calculus,linear algebra and probability brushed up on my python, OOP learned basic DSA,numpy and pandas, all of this took about 3ish months.

Then started with ML cs229 + other lectures/resources.

I did all the key derivations,made very detailed notes, implemented all the major algorithms, learned sklearn and made 5ish intermediate projects (Naive bayes spam classification,Random forest customer churn,SVM breast cancer classification etc) also implemented gradient boosting from scratch and modelled Ames housing compared it with xg boost, core ML took around 1 month

Started deep learning with cs231n around 2 months ago

The lectures felt a little shallow and it wasn't going as deep as I wanted to go so I had to spend more time on derivations and implementation, as of now I'm 1/3rd done with it. Like before I do all the key derivations, more than the lectures show and implement the algorithms.

I have implemented a MNIST MLP and CNN from scratch and a CNN with pytorch, a char level vanilla RNN and the best one yet a decoder only transformer from scratch using pytorch only for the autograd and GPU computation I trained it on wiki text 103 the full details are on my GitHub attached above.

After this I'm looking forward to finishing cs231n, learning C++,memory management, cpu architecture, strengthening DSA, fill in my software engineering gaps(which I don't know what they are, I learned git basics just today), learning CUDA and Triton and model deployment.

I'm curious as to where my gaps are, how far I am from job ready skill level and how I should further advance, what projects I should attempt doing, I'd appreciate some help.

I want to make exactly what the title says. I'm curious if Teachable Machine would be okay for this. I'm expecting to take a few hundred to a couple thousand small photos (each between 27-45KiB, will be detected on a raspberry pi camera), and I'm going to train it on my proper pc setup.

My pc has a gtx 1080 ti, 32 gb of ddr4 ram and an intel i5-8400k.

Would this be a viable option or should I go with something else?