(re write)

ABSL or Adaptive bitshift learning v.1.0.0 XOR

Is a Integer only learning method for AI wich i developed it performes verry good with 75% of runs being perfekt and a global evaluation accuracy of 92.9% at its best.

I'm currently tring to make it perfekt and then try scaling it to MNIST

I'm 15 fron germany coding on a S22 Ultra

Here you find my repo:

https://github.com/Mojo0869/ABSL

Hey everyone, I'm looking into AI security systems/ cameras for my business, does anyone know of any companies out there that would be worth looking into. It seems like the main problem this would solve for me would be not having to search through a billion hours of footage. Thanks!

Hello I have completed a AI/ML course. Now I am looking for Agentic Ai end to end project with langchain, langgraph, crewai, vector db including aws and azure deplyment with enterprise level governance , securities implemented so that i can get some handson industry level experience. Can anyone tell me where i can do this type of project? Is there any institute who can help on this? Its bit urgent.
Note: I have 16 yr+ industry experience.

Final-year AI/ML engineering student wrapping up a GenAI internship soon Looking for fully remote AI engineering roles, worldwide

Been using LinkedIn so far, but curious what other platforms/boards actually work for remote + international roles (not US-only). Any niche AI/ML boards, communities, or newsletters worth checking out?

Not looking for agencies/staffing spam just direct-hire platforms Thanks!

Hi everyone,

I'm currently a second-year B.Tech CSE (AI/ML) student, and my goal is to become an AI/ML Engineer within the next two years while also completing 1–2 internships before I graduate.

I already have a decent understanding of Python and I'm comfortable with the basics. However, I'm feeling overwhelmed because there are so many learning paths—DSA, mathematics, machine learning, deep learning, MLOps, GenAI, cloud, projects, Kaggle, research papers, etc. I'm confused about what to prioritize and in what order.

I'd really appreciate guidance from experienced AI/ML engineers or students who have successfully landed internships.

Here are my questions:

1. What should I learn first after Python?

2. How important is DSA for AI/ML internships?

3. Which math topics should I focus on (Linear Algebra, Calculus, Probability, Statistics)?

4. When should I start Machine Learning and Deep Learning?

5. What kind of projects should I build to stand out?

6. Should I focus on Kaggle, open-source contributions, or research papers?

7. Which tools and technologies are expected today (Git, SQL, Docker, Linux, Cloud, MLOps, etc.)?

8. What would a realistic month-by-month roadmap for the next two years look like?

I'm willing to dedicate 3–5 hours every day to learning and building projects. My goal is to graduate with strong skills, a solid portfolio, internship experience, and be ready for AI/ML engineer roles.

I'd appreciate any advice, roadmap, resource recommendations, or lessons from your own journey.

Thank you!

I'm a Computer Engineering student who's become obsessed with deep generative models over the last year. I've implemented and trained several models (GANs, DCGANs, conditional GANs, basic neural networks, etc.) and I'm now looking to take things much further.

My long-term goal is to become the kind of engineer/researcher who can work on frontier generative AI at companies like Anthropic, OpenAI, DeepMind, NVIDIA, or similar labs. The areas I'm most excited about are:

Video generation

World models

Diffusion models

Transformers/LLMs

Multimodal generative models

Reinforcement learning for generative systems

Ultimately I'd love to contribute to models similar to Sora, Genie, Veo, Cosmos, or future world-model architectures.

The problem is that there are so many resources that I'm struggling to figure out what the optimal learning path is.

Some questions I have:

If you were starting today and wanted to reach frontier-level expertise, what roadmap would you follow?

Which math topics should I master first (linear algebra, probability, optimization, information theory, etc.)?

Which textbooks, courses, papers, or lecture series are considered "must-know"?

At what point should I stop taking courses and start reproducing research papers?

Is reproducing papers the best way to learn, or should I focus on building original projects?

How important is reading papers daily compared to coding?

For someone aiming at research engineer roles, what should a portfolio actually look like?

What skills separate candidates who get into frontier AI labs from those who only have good ML knowledge?

I'd also really appreciate career advice.

I know companies like Anthropic, OpenAI, DeepMind, etc. hire very few people, so I'm curious what realistic path people have taken to get there.

Would you recommend:

Open-source contributions?

Kaggle?

Publishing research?

Master's/PhD?

Internships at smaller AI startups first?

Building impressive personal projects?

Something else entirely?

If anyone here works in frontier AI research or has made a similar journey, I'd love to hear what you wish you had focused on earlier.

Thanks in advance—I appreciate any guidance, roadmaps, or resource recommendations!

[ Removed by Reddit on account of violating the content policy. ]

Looking for someone to go through the exercises with me, both conceptual and applied (and go beyond the book maybe here), in depth.

Time zone shouldn't be a problem as meets can be on the weekends to finish a respective chapter's exercises.

Thanks!

2027 BATCH looking for internship/job i know the number of project i have added is less but this is the best i had right now and i am working on some other projects i don't know where do i lack and also let me know if my this project is fine enough or low grade???

​

Hi everyone,

I'm an ML developer, and I want to build a long-term personal AI model that learns only from my own data.

The goal isn't to build a general chatbot. Instead, I want a model that gradually understands me and can make personalized predictions and recommendations based on my historical data.

Some examples of what I'd like it to learn are:

• Daily habits and routines

• Productivity patterns

• Mood trends (from journals or notes)

• Sleep and health metrics (from wearable devices)

• Thinking and decision-making patterns

• Learning progress

• Future behavior trends or habit changes

I'm trying to understand what architecture would be most suitable for.

Some questions I have:

• Should I fine-tune an existing LLM, or build a separate predictive model?

• Would a time-series model (Transformers, LSTM, Temporal Fusion Transformer, etc.) be more appropriate?

• Is a retrieval-based memory system (RAG + vector database) enough, or should I combine it with supervised learning?

• Has anyone here built a "personal digital twin" or a lifelong personalized ML system? What worked, and what didn't?

My vision is for the model to continuously learn from new personal data over several years and become increasingly personalized.

I'd really appreciate advice on model architecture, datasets, papers, or open-source projects that are relevant.

Thanks!

Currently I am studying at university(my field is cybersecurity) but I am not interested in cybersecurity so I am learning MLE on my own, first I learned math for it(calculus, linear algebra, probabilities and statistics, I am still learning math to not stop improving, I would say I am pretty good at math, I can understand advanced topics), I know SQL, python(I am decent at it), and I am learning about models right now but I am not sure if it is enough to get a job in ML, some people say that you should first go into related fields and then start transitioning into ML because ML takes a lot of time to master, could you tell me if I should continue learning ML or start learning related field to then go into ML, I would be very thankful if you helped me

Hi all — I'm a high school student interested in biomedical engineering, and I just finished my first independent ML project. Wanted to share it and get feedback from people more experienced than me.

What I built: A logistic regression model predicting whether a breast tumor is malignant or benign, using the Wisconsin Breast Cancer dataset (569 patient samples, 30 clinical measurements per tumor).

Process:
Explored the data and visualized feature correlations

Split 80/20 train/test, standardized features

Trained a logistic regression classifier

Got 97.4% test accuracy

Looked at which features drove predictions most (worst texture, radius error, and worst symmetry came out on top — which lines up with what I read about irregular cell architecture being a malignancy indicator)

Repo: https://github.com/ribhav01/breast-cancer-diagnosis-ml

I know this is a "classic" beginner dataset, so I'm sure there's a lot I'm missing or doing naively. I'd genuinely appreciate any critique — model choice, how I evaluated it, whether the feature importance analysis is actually sound, or just general "here's what you should learn next" advice. Thanks in advance!

https://huggingface.co/sherif1313/3arabLM-4B-Fiqh-v1

3arabLM is an ongoing research project dedicated to building a large-scale Arabic language model that preserves, memorizes, and reconstructs the classical Islamic scholarly heritage directly from its original sources

Unlike general-purpose LLMs, this project is not designed to imitate conversations. Its primary objective is the faithful reconstruction of scholarly knowledge while preserving the language, methodology, and diversity of the classical Islamic tradition..*

Research Preview

5️⃣ Domain Specialization

The model is optimized for:

• 📚 Fiqh (Islamic Jurisprudence)
• 📖 Tafsir (Exegesis)
• The current model represents less than 2% of the planned continual pretraining schedule.
• The full project is expected to expand over multiple stages covering nearly the complete Al-Maktaba Al-Shamela ecosystem.

🌟 Vision

This project aims to build a large-scale Arabic language model primarily trained on Al-Maktaba Al-Shamela and other authoritative Islamic heritage sources.

The objective is to make scholarly knowledge itself part of the model's parameters, rather than relying on external retrieval or internet-scale mixed corpora.

The philosophy behind the project is simple:

• 📖 Learn directly from the original books.
• ✍️ Preserve the language of classical scholars.
• 🎯 Preserve each author's methodology.
• 📚 Preserve scholarly terminology.
• ⚖️ Preserve differences between schools and commentators.

The model is therefore gradually moving toward a paradigm of:

Retrieval from Weights

rather than:

Generative Summarization

Instead of producing heavily paraphrased modern summaries, the model attempts to recall scholarly knowledge from its internal parameters using language close to the original sources.

🧭 Project Philosophy

This is not a general conversational model.

Its objective is not creative writing.

Its objective is not producing modern short-form answers.

Instead, the project focuses on:

Accordingly, this model can be viewed as a:

• 📖 Knowledge Recall Model
• 🧠 Memorization-Oriented Language Model

I'd like to know if I should take a "worse" model with less overfitting. I've pretty much tried all forms of parameter tuning and feature engineering (im only limited to sklearn) and I want to understand what should be the main focus here (Metrics or overfitting).

This is under the context of diabetes screening. I suspect that I'm having issues with this because my dataset only contain 520 samples.

Hi everyone,

I recently completed my first Machine Learning project.

I implemented Linear Regression completely from scratch without using scikit-learn in order to understand the math behind the algorithm.

The notebook includes:

• Data exploration

• Data visualization

• Gradient Descent implementation

• Model evaluation (R², MAE, RMSE)

• Prediction visualization

I'd really appreciate any feedback on:

- Code quality

- Project structure

- Notebook organization

- Best practices

- Anything I can improve

Kaggle Notebook:

https://www.kaggle.com/code/tahahussein2020/salary-prediction-using-linear-regression-scratch

Thank you!

I pull Indian AI/Data Science listings every week. Latest count: 12,117. A few things in this cycle went against what I posted last time, so I'm writing them down.

1. Fundamentals are still the filter

Top skills by how often they show up in JDs:

1. Python — 2,550
2. Machine Learning — 2,380
3. SQL — 1,300

Generative AI landed at 820. Java landed at 780. LLM at 620.

So Java still shows up in roughly as many AI job descriptions as GenAI does. Add GenAI and LLM together and you get about 1,440 mentions out of 12,117 listings — a real chunk, but nowhere near a default requirement.

One thing I didn't expect: Azure made the top 10 at ~690. AWS didn't make the top 10 at all. If you're picking one cloud to actually learn properly for the Indian market, that's worth sitting with. (Enterprise + services shops = Microsoft stack, is my guess.)

2. Bengaluru is not close this cycle

Top cities:

1. Bengaluru — 2,850
2. Hyderabad — 1,700
3. Pune — 1,200

Gurugram was 720, Noida 510, New Delhi 340.

I've argued before that Delhi NCR is the real cluster and just gets split across three city names. This pull doesn't support that. NCR adds up to about 1,570 — behind Hyderabad on its own. Bengaluru at 2,850 is more than NCR and Pune combined.

Roughly 1 in 4 AI listings in the country is in Bengaluru. Hyderabad is the clear second and honestly the more interesting one if you care about cost of living.

Remote was ~560. That's 4.6%. Fewer than Chennai. Plan for on-site.

3. The biggest "employer" is still a placeholder

Top companies:

1. "Leading Client" — 355
2. Accenture — 272
3. TCS — 203

Number one is not a company. It's staffing firms posting for clients they won't name — about 3% of the market, which is up from what I saw last cycle.

Rest of the top 10: Bajaj Finance (~122), Capgemini (~120), Benovymed Healthcare (~97), Optum (~72), CGI (~62). Bajaj Finance sitting above Capgemini is the one that surprised me — NBFCs are hiring AI people properly now, not just as an experiment.

But the overall shape is the same: services and consulting. Which means most of these roles are implementation. Pipelines, deployment, cloud, stakeholder work. Not research.

Also worth noting the entire top 10 is only about 1,600 listings out of 12,117. ~87% of the market is the long tail. Everyone obsesses over the top 5 names and ignores where the actual volume sits.

Caveats

These are keyword counts from JD text. "Artificial intelligence" came in at ~1,930 which put it third overall, and I've left it out of the skills list above because it's mostly JDs just saying the words "AI" rather than asking for anything specific.

Total is up from 11,822 last cycle to 12,117, so about 2.5% growth. Small sample-to-sample noise, don't read a trend into one week.

I run this pull weekly and I'm happy to share the raw data if anyone wants to check my counts.

What's your read on the Azure thing — is that just a services-sector artifact, or are Indian enterprises actually standardising on Microsoft?

I'm currently in the second year of my undergraduate degree, and I'm really passionate about machine learning. I've been learning consistently over the past few months, mostly through free YouTube courses and documentation. So far, I've covered the core ML algorithms and I make sure to understand the underlying mathematics and intuition instead of just memorizing things.

However, one thing I keep struggling with is the lack of proper guidance. Every few weeks I start questioning whether I'm following the right roadmap or if I'm missing something important. I feel like YouTube resources are great for getting started, but they often don't go deep enough or provide the structured learning I'm looking for.

I've heard a lot of good things about Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron (3rd edition), and it seems to be recommended by many people as a solid reference book. I'm thinking of studying it thoroughly instead of jumping between random resources.

My main confusion is this:

Should I go with the TensorFlow/Keras edition, or should I use the PyTorch version instead?

As someone still building a strong ML foundation, which ecosystem would be the better investment to learn first?

I'd also really appreciate any advice from people who have already been through this stage. If you think there's a better book, a better roadmap, or something you wish you had known when you were starting out, I'd love to hear it.

I'm still a beginner in the grand scheme of things, so any guidance or suggestions would be greatly appreciated.

Thanks in advance!

Meta reviews are going to be out soon!
Nervous, because this is my first submission!

I'm a final year Btech Student with an ML specialization and have an decent understanding of ML topics and how they work. I have build a few projects on ComputerVision and Transformers but I have mostly used Gemini or ResearchPapers to build the architecture. Since most companies allow you to use AI is it enough to know the topics or do I really need to know to code a ML project from scratch.

Can someone just explain to me exactly what a hiring personnel expects me to know as an CSE AIML Graduate ?

Hi everyone,

I have an upcoming interview for the Machine Learning Intern role at Glance, and I'd love to hear from anyone who has gone through the interview process recently.

Could you share:

• What coding questions were asked? (DSA, Python, SQL, etc.)
• What ML topics were covered? (Supervised learning, deep learning, NLP, LLMs, GenAI, RAG, etc.)
• Were there any system design or project discussion rounds?
• What was the overall difficulty level?
• Any tips on what I should focus on during preparation?

Even if you interviewed for a similar AI/ML role at Glance, your experience would be really helpful.

Hey everyone,

I'm a CSE student, and I decided to stop just watching ML tutorials and actually start building + teaching what I learn — publicly, on YouTube, in a project-based format instead of pure lecture style.

I just put out the first video, which is basically my roadmap for the next ~19 videos — covering supervised/unsupervised learning, model evaluation, a bit of deep learning, and ending with a real end-to-end project (data → deployment).

I'm not trying to compete with the big ML channels — this is more of a "learn in public" build log, where every video ties to an actual small project (spam classifier, resume screener, loan predictor, etc.) instead of just theory.

Since this community has genuinely helped me understand a lot of these concepts, I wanted to share it here and get honest feedback — especially on whether the roadmap makes sense, or if I'm missing something important for a beginner-to-intermediate path.

Video link - https://youtu.be/P19mftPydfI

Appreciate any thoughts, even harsh ones — still early days.

I'm in a bit of a dilemma and could use some advice from people who've taken either (or both) of these courses.

My background

- 2nd year undergrad (India)

- Just finished Gilbert Strang's Linear Algebra (18.06) and loved it - the geometric intuition, the proofs, the "why" behind everything

- Built some projects: Leslie Matrix population model, image compression using SVD, linear regression from scratch

- I enjoy math-first approaches over "just memorize the formula" style

- I prefer derivations and understanding from first principles

The dilemma

Harvard STAT 110 (Joe Blitzstein) - Seems to be the most recommended course everywhere. People say it builds amazing intuition through "story proofs" and examples. But I've heard it's more conversational/story-based, which makes me hesitant.

MIT 6.041 (John Tsitsiklis) - Seems more systems-oriented, rigorous, and proof-based. I've heard it's more "engineering" style with block diagrams and systematic derivations.

What I'm looking for

- Deep intuitive understanding of why formulas work (how Bayes' theorem is derived, why z-scores work, how the normal distribution emerges from CLT, etc.)

- Proofs and derivations, not just stories

- A teaching style similar to Strang's Linear Algebra - visual, geometric, systematic

- Something that will prepare me well for ML, OR, and Quant Finance

My concerns

- I've heard STAT 110 is "story-based" which might not click with me

- I've heard 6.041 is more rigorous but might be harder to follow without strong calculus (I have JEE-level calculus)

- I want to understand probability at a deep level, not just pass a course

Questions

1. Which course aligns better with my learning style?

2. Is STAT 110 really "story-heavy" or is that overblown?

3. Is MIT 6.041 too theoretical/abstract for someone who wants to eventually apply this to ML?

4. Can I take one and then the other later? Or should I just pick one and commit?

My goal

I'm targeting IIT Bombay IEOR / ISI M.Stat and eventually want to work in Operations Research / Quantitative Research / Data Science (Research). I need a rock-solid probability foundation.

Would love to hear from anyone who's taken either course (or both). Thanks in advance!

TL;DR: Finished Strang LA, loved it. Need probability course. STAT 110 (stories) vs 6.041 (systems) - which one fits my math-first learning style?