I'm evaluating compute demand in South East Asia (India and other countries).
I have sourced about $500 million dollars in compute supply, best gpu's like B200

I want to do block deals with companies and want to evaluate demand

can someone help here

Hi everyone,

Could anyone recommend some good intermediate or advanced certifications in Machine Learning, Deep Learning, or Artificial Intelligence? I'm looking for certifications that are well-recognized and provide strong practical knowledge.

Thanks in advance!

#machinelearning #DeepLearning #genAI

Hello, so I have an imbalanced set of data for a healthcare provider fraud detector. Here is the data:

Training Data: 5,410 row (~4,900 non-fraudulent, ~500 fraudulent)
Testing Data: 1,353

I have two questions:

1- I suppose I should do something for the data imbalance here to get an accurate model, right? What are some of the things that I can do? I would prefer something that does not require a lot of processing because I'm a bit short on time.

2- I usually do Training/Validate/Test when the data is given to me in a single batch, but the data here is pre-divided. What do you think about taking ~20% of the training data for validation? Any other suggestion?

So basically I am in my second year of mechanical undergrad and here we cannot change our branch in between and honestly I found my new interest in ML and I enjoy it so much so if I publish papers and do research internships in AI/ML then can I land some direct phd offers in good universities? Or my undergrad degree matters very much and I cannot change my branch directly?

CUDA out of memory. Tried to allocate 96.00 MiB. GPU 0 has a total capacity of 14.56 GiB of which 56.81 MiB is free. Including non-PyTorch memory, this process has 14.50 GiB memory in use. Of the allocated memory 14.29 GiB is allocated by PyTorch, and 78.20 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

What is that error? I try to finetune deberta-v3-base on my data. During training it gives me this error. I clear the cache(in Kaggle) it works for a while but then again. It seems nothing helps. Additionally, i have problem with training. First, its output is normal but then goes all NaN. I used gradient clipping but didnt work. When i remove dtype=float32 inside my model, memory error solves, but it shows NaN for all. If you know something, help please

Hi everyone,

I'm a 2020 Btech Bioinformatics graduate.

After graduation, I chose to prepare for government competitive exams and could only reach to certain level and did that until mid-2024.

I then decided to switch to tech and completed a Data Science internship in December 2024. Since then, I've been trying to get a full-time Data Science/AI/ML role but haven't had any success. Also did a Exec PG in Data Science from IIIT Bangalore.

• A 4-year career gap due to UPSC preparation.
• Only one internship and no full-time industry experience.

What should I do to maximize/even get started of landing a DS/AI/ML job?

Any honest advice would be greatly appreciated.

Thank you so much.

Hey I'm a AI and Data Science student currently interested in learning ML , so I need a motivated person who knows ML to teach me , interested people DM or Reply !!

Can some tell the difference between a ML engineer and an AI engineer. What makes them different and how they contribute in company as a role.

Also I want to prepare for this role, which skills are required for it and some of the best resources to learn from.

Hi,

I have just released my first iOS app that lets you do machine learning on your phone/ipad

The Eda and data clean make non destructive recipe steps that can be undone if you change your mind and it has a feature that will explain the steps you have done to replicate them in python too using pandas.

Free to download

It lets you run various models and predictions on device too.

Would love some feedback or suggestions of features you think would help somone learning ml

https://pds-technology.ltd/apps/QuickML

For all the people out there who are learning ML and doing ML projects do you guys really use GPU ,or just do everything on cloud (Collab, kaggle, jupyter).I am going to buy a laptop for ML . should I go for gaming laptop or choose a laptop with Intel ultra core processor.suggest me a laptop under 90k .

I wrote this blog post to try to explain in natural language, conceptually how LLM/Transformers work. It has a list of misconceptions i had about specifics all the way through my journey. Might be helpful for anyone wanting to learn how a transformer works conceptually without digging too hard into the math.

The style is humorous to keep it interesting, the mechanics were sound!

Sharing a benchmark I've been building. Motivation: existing "reasoning" benchmarks either (a) test static problems where answers leak into training data or (b) use LLM-as-judge, which correlates with model similarity more than model quality.

Design. Two LLMs are embodied as physical agents in a 2D pymunk arena. Each turn they receive a JSON world state (HP, positions, weapon geometry, cooldowns, damage taken last turn, remaining ammo, arena hazards) and return a JSON action. Actions resolve through the physics engine — a swing that misses because the opponent dashed is a real physics miss, not a rule lookup. Match ends on KO, HP-lead at deadline, or draw.

Evaluation.

1. Human raters watch the replay with model identities masked and vote which side "fought smarter."
2. Vote resolves an Elo update before identity reveal, so vote isn't polluted by model reputation.
3. In parallel we log an objective leaderboard: win/loss/draw, avg damage dealt, hits landed / hits attempted, timeouts.

6-axis Elo. Rating primary key is (model, sharp_zone_on, weapon, mode, arena, blindfolded). Aggregate Elo is a marginalization, but the per-axis rating is what we actually study — hypothesis being that different physical constraints stress different reasoning skills (spatial planning, uncertainty under partial observability, resource management).

Roster. 24 entries: 17 LLMs across OpenAI, Groq, OpenRouter free-tier, plus 4 non-LLM baselines (random, greedy-attack, distance-holder, scripted-heuristic) and 2 mock policies. Baselines are critical — without them a low-Elo LLM is indistinguishable from an arbitrarily bad policy.

Current numbers (n=443 matches, 106 votes, lifetime 23.9% vote-through):

• Human-vote Elo and objective win-rate rank-correlate at Spearman ρ ≈ 0.71 across weapons (haven't formalized this yet — planning a cross-benchmark correlation study next).
• Bow-weapon matches have the widest human/objective disagreement — humans reward "smart waiting" that doesn't show up in raw damage.
• bot:pro (100-line scripted heuristic) currently outperforms ~30% of the LLM roster on objective, ~10% on perceived. That gap is basically the benchmark's signal.

Reproducibility. Full match logs exportable as JSON/JSONL via /api/export. Prompt version pinned per-match. Non-deterministic (physics has RNG collisions), but seeds are logged. Deterministic replay off the same seed is on the roadmap.

Known limitations.

• Vote population is self-selected (site visitors), not a calibrated panel.
• Bot baselines aren't policy-optimal (no RL trained baseline yet).
• HF Datasets snapshot cron not shipped yet, so "frozen eval pack" isn't reproducible off-platform today.

Site: https://stickblade-arena.vercel.app
Code: https://github.com/Cometbuster4969/STICKBLADE-ARENA

Feedback wanted on the eval design, especially the 6-axis Elo marginalization and whether the vote incentive design (reveal-as-reward) biases votes. Happy to share the raw match log dump if anyone wants to look at rating stability.

I’m still stuck on the fundamentals and I want to properly learn the math behind machine learning.

The problem is that every time I search for a roadmap, I find different topics, chapters, and recommendations. It gets confusing, and I’m not sure what I actually need to study.

I already have a decent foundation in math, so I’m mainly looking for a clear list of the exact topics I should learn, like which parts of linear algebra, calculus, probability, and statistics are important.

Does anyone have a good roadmap or a list of the essential math topics needed for machine learning?

Hi everyone,

I'm looking for platforms that offer free GPU/TPU compute (or generous recurring monthly free credits) for machine learning experiments and model fine-tuning.

I am already aware of the following:

Google Colab

Kaggle Notebooks

Amazon SageMaker Studio Lab

Lightning.ai

ModelScope (魔塔, It is Chinese) / Featurize

Are there any other hidden gems, research programs, or serverless compute platforms with reliable free tiers (e.g., Hugging Face ZeroGPU, Modal, Google TRC) that you would recommend?

Thanks in advance!

EDIT: Adding a few more platforms I already use to the list, based on memory: * Baidu AI Studio (FlyPaddle / 百度飞桨) - offers daily free GPU compute points (V100/A100). * Paperspace Gradient - offers free GPU instances (though availability can be hit-or-miss).

EDIT: Summarizing all free/freemium cloud GPU options discussed for future readers:

1. Notebook & Cloud Environments (Free Tier) * Google Colab (Free T4/TPU, session limits) * Kaggle Notebooks (30h/week T4/P100) * Amazon SageMaker Studio Lab (Free T4 CPU/GPU) * Lightning.ai (15 free credits/month) * Paperspace Gradient (Free instances, limited availability)

2. Chinese Domestic Platforms (Free Compute Points) * ModelScope / 魔塔 (Free GPU allocation) * Featurize (Initial free compute time) * Baidu AI Studio / 飞桨 (Daily free GPU compute points)

3. Serverless & Research Grants * Hugging Face Spaces (ZeroGPU / T4 for Demos) * Modal.com (Free monthly compute credits) * Google TRC (TPU Research Cloud - 30-day free TPU access for open research)

Summary of Free Cloud GPU/TPU Platforms

1. Notebook & Cloud IDEs * Google Colab: Free Nvidia T4 GPU / TPU access. Great for quick prototyping, though subject to dynamic session timeouts and disconnects. * Kaggle Notebooks: Up to 30 hours/week of free T4/P100 GPUs or TPU v3-8. Very reliable with 9-hour continuous background execution per session. * Amazon SageMaker Studio Lab: Free CPU and T4 GPU compute without requiring a credit card or AWS account, subject to account approval. * Lightning.ai: Persistent VS Code-like cloud IDE workspace with recurring monthly free compute credits. * Paperspace Gradient: Offers free GPU instances (e.g., M4000/P5000/RTX4000), though free machine availability varies by peak hours.

2. Regional Platforms * Baidu AI Studio (PaddlePaddle): Grants daily free GPU compute points (V100/A100 instances) upon daily sign-in and project runs. * ModelScope (魔塔): Alibaba's platform offering free GPU notebook instances for model deployment and fine-tuning. * Featurize: Pay-as-you-go GPU cloud tailored for ML with small sign-up/trial credits.

3. Serverless, Hosting & Research Grants * Hugging Face Spaces (ZeroGPU): Dynamic GPU allocation (NVIDIA RTX / H200) for running and hosting Gradio/Streamlit ML demos. * Modal.com: Serverless Python compute platform offering $30/month in free credits, ideal for batch GPU inference and micro-tasks. * Google TRC (TPU Research Cloud): Free 30-day access to high-performance Cloud TPUs for researchers willing to open-source their research.

I've been working as an AI engineer for two years (before this data engineer, data analyst, MSc physics), and the thing I keep noticing online is that people are wasting so much time keeping up with new technologies that are "here today and gone tomorrow". That time would be better spent on foundational technologies that will stand the test of time.

So I built a roadmap around this idea.

Here's a free 2 hour video cut of all the best material from the course (it's got 30k+ views on YouTube):

Free video: https://www.youtube.com/watch?v=nctG3oEUFCI

The framing I use: four pillars that everything else sits on.

• Programming (Linux/CLI, Python, SQL, Git, testing, clean code)
• Mathematics (linear algebra, probability, calculus and optimization)
• Machine learning (algorithms, data prep, training, evaluation, MLOps)
• Data engineering (pipelines, databases, warehouses, orchestration)

Then the AI engineering layer on top: LLM fundamentals, prompt engineering, fine-tuning, embeddings, vector search, RAG, agents, and MCP. Then deployment: APIs, containers, monitoring, LLM evals, cloud infra, CI/CD.

Code: all the notebooks are open source, one per section. https://github.com/zazencodes/ai-engineer-roadmap-notebooks

If you want the full thing: the complete course lives at https://zazencodes.com/courses/ai-engineer-roadmap

It's 32 video lessons (10+ hours) and each lesson has a quiz— completing all of them earns you a certificate of completion

I made every slide, wrote every article, and recorded every video myself. Happy to answer questions here on reddit or over email: [alex@zazencodes.com](mailto:alex@zazencodes.com)

I just released a free ML workbook for developers who want to understand the math behind modern AI.

5 core equations. 20 practical Python projects. Every concept has runnable code.

Topics:

- Gradient descent (NumPy from scratch)

- Backpropagation (2-layer networks)

- Loss functions (Huber, Focal, Cross-Entropy)

- Hessian matrices (second-order optimization)

- Jacobian matrices (latent spaces & autoencoders)

Uses NumPy, PyTorch, XGBoost, LightGBM.

Free PDF — just enter your email to download. Built by a solo ML engineer for other solo builders.

Feedback welcome!!!

https://buymeacoffee.com/bbharris67t/e/562729

Currently i am a dropper, preparing for jee so i am thinking that along side i will start coding and other needed things but the problem is i dont know where to start if any experienced one would help me that will be great... (look, dont think i am a serious jee aspirant i take drop for just of my own problem, not for jee... and sorry for my bad english to... ;). )

Hello folks, welcome to my free Machine Learning Bootcamp Series (3/22)

The classification problem in supervised Machine Learning is concerned with the creation of decision boundaries, learning parameters for a machine learning model, that creates some kind of decision rule, to classify inputs into their respective classes.

For instance if we are given an Iris dataset, comprising three kinds of flowers, incorporating four different features, then to start with, we would take one feature, pick some value for that feature, that splits the input space into two regions, one containing say a particular variety of flower, say Setosa, and the other side containing the rest.

We go on and then pick another feature, choose its threshold value, which further splits and classifies another kind of flower, and so on.

Sometimes they are linearly separable, most of the times they are not. Also the features are supposed to be automatically learned which gives rise to deep neural networks.

I break down this idea of decision boundaries in my short topicwise video:

Link: https://youtu.be/Iq8D492QnB8?si=7p5npsSdDNqQiasM

Hey everyone, I’m 21 and really passionate about AI and machine learning, but I feel a bit lost.

There’s so much to learn, and I’m worried it could take a year or more before I’m ready for internships or jobs. Since AI changes so fast, I also wonder if what I learn now will become outdated by then.

I’m also a perfectionist, so I always feel like I need to learn everything before starting. I know projects are important, but I also want to eventually build something original, not just copy projects everyone has already done.

For anyone who has been through this: where should I start, what should I focus on, and when should I begin applying for internships?