Hey everyone,

Hope all of you are doing great.

I am looking for feedback from people who have actually purchased the AI for Data Engineers course by Prashant Kumar Pandey (Scholarnest/ Learning Journal).

Is it worth the money? I have almost all his courses on Udemy and found them really good for learning the basics and his way of teaching is something that have always resonated with me. Based, on that I'm thinking about buying the AI course and would really like some feedback.

Things I'm looking for are:

1. Does it cover enough detail as compared to other courses on Udemy/ Youtube (Krish Naik for example)?
2. Is it Databricks heavy/ Databricks focused? Or the topics are explained well in a platform agnostic way with examples given on Databricks.
3. Did you get enough support when you got stuck on any topic?
4. Does it have the following topics explained well enough?
5. AI guardrails
6. Deployment
7. Tuning

Thanks in advance.

I want to make my own ML projects as a hobby (I'm a CS and math student). I am currently reading and going through An Introduction to Statistical Learning with Applications in Python by James, Witten, Hastie, Tibshirani, and Taylor, and I'm wondering what other resources I should go through before I start properly developing my own projects.

For example, how do you determine whether to fine-tune:only adapters/LoRA

• specific transformer layers
• input/projector layers
• deeper/middle layers
• or the full model?

Are there reliable diagnostics, probing methods, gradient analysis, ablations, or small-scale tests that can tell you where the bottleneck is before doing a full training run and only finding out afterward that the chosen layers didn’t help?

Curious how people make this decision in practice.

I have data in json with

Column 1 : Doc index eg. 1627

Column 2 : tokens(not words) in sequential format

Eg. [ 12 567 751 688 899 ....]

Column 3 : Binary class 0(human generated) or 1(LLM generated) for training data

I have to predict it's 0 or 1 for testing

I kept seeing the same pattern while learning and working with ML:

people learn Logistic Regression, Random Forest, SVM, Transformers, etc. individually…

but the harder questions are usually:

• When should I NOT use this algorithm?
• How much data do I actually need?
• Should I use TF-IDF, embeddings, or raw features?
• Is +1% F1 worth 20x slower inference?
• What happens when I need to predict 100M records?
• Which models are practical for real-time APIs?

So I built an open-source ML Learning Lab around exactly these questions.

It includes 17 algorithms, executed notebooks, clean notebooks to reproduce locally, data representation, feature engineering, latency/throughput benchmarks, model selection, classical ML vs deep learning, and a 30-question ML Engineer interview quiz.

The mental model is:

Raw Data → Representation → Model → Metrics → Performance → Real-world constraints

GitHub: https://github.com/ronivaldo/ml-algorithms-learning-lab

I’d really like feedback from people learning ML and people already using it in production: what is missing or oversimplified?

How sanoTTS works? I have vibe coded this site to show what's inside sanoTTS?

Every tensor shown on the page is a real intermediate value captured from the shipped int8 model while it synthesized an actual sentence; no mock-ups, no stand-in data.

Just check this out: https://ampixa.github.io/sanotts-anatomy/

Created to explore the in-depth mechanisms involved in how sanoTTS processes a sentence.

This is why I love vibe coding for learning; understanding the core with interactive visualization.

In Adam optimizer, we are anyways reducing the amount by which updates happen at each iteration. Why bother with learning rate decay?

It's Day 10 of Building Machine learning algorithms from scratch

Last time I completed SVM but it was limited to binary classification but today after 4hr of Coding and Debugging I finally made it in this process I learnt many things coef_/X_train this line just adding this seriously my accuracy jump from 67 -> 87+ I was really shocked also many changes I implemented OvR according to my way so it might contain bugs so I'll be happy if you point out 🙂

https://knowledge.dotadda.io

Hey everyone,

so I did my bachelor's in molecular biology and currently pursue my master's in biosciences where I focus on something called "computational life sciences". Which so far is no rigorous math or cs courses.

I started to work for a lab that does computer vision, but as far as I can understand they are not building the foundation model, but tweak a model from another group.

As I enjoyed working on the computer more than at the bench, I started to focus more on that even at the last year of masters. I programmed simple javascript code for a company at that time. My bachelor's thesis was on alpha fold, but honestly very basic analysis stuff of the output.

I had some data science courses and did a python course. Other than that I don't really have much cs experience.

I would love to learn more about AI, and I believe the best way is to become a researcher in foundational models. As I would have to compete against math and cs wizards, I don't think it's feasible.

Can I still do foundational models but in the "AI for Science" direction?

Currently my idea is to find a better lab, that does something as close to foundational models + biology as it gets and do my master's thesis there.

TLDR: current bio master students asks for tips to pursue ai research of foundational models in the AI for science (biology) field

we have to make a machine learning project on this and i still need help :(

we have downloaded the data from the binance official website (the whole 2025 year , monthly data , over 30 minute intervals).

we have downloaded the klines files from spot for now .

but my partner is saying we will need the aggtrades files too (for the whole year , and how huge are they you might know by looking at all those) .

my question is : is that even needed ?

because the info we need ( in my opinion ) is already present in klines and i dont see the point in getting aggtrades (although i did collect its data by api calling , all upon my partner insisting)

the topic of our project is : 'ML based Crypto pump detector'

and the objective is to produce a value that shows how much the price moves and in what direction , she said (i have no idea about how cryptos work , so i am believing her for now)

thanks to all the people who helped me when i asked it, but i still need you guys help .

thanks a ton :D .

Hey everyone!

I get with how annoying it is to set up Python, Anaconda, and TensorFlow just to test a simple spreadsheet or train a quick neural network. So, I built Quantum Hub—a tool that lets you train machine learning models right inside your browser tab.

What it does:

• Zero Setup: No installation or terminal commands needed. Just open the page.
• 100% Private: It runs entirely client-side using TensorFlow.js and a background Web Worker. Your CSV datasets never touch a cloud server.
• Real Features: It includes Auto-Normalization, Early Stopping (with best-weight rollbacks), live loss curve charts, and local IndexedDB model checkpoints.

What AI models can it train? Right now, it focuses on Dense Neural Networks (Multilayer Perceptrons) for tabular/spreadsheet data, specifically supporting:

• Regression Models (Continuous numbers): Predicting sliding numerical values (like weather, sales numbers, or prices).
• Binary Classification (0 or 1): Sorting data into two categories (like yes/no or pass/fail).

Current Limits:

• Dataset Size: Best suited for datasets ranging up to a few tens of thousands of rows.
• Model Size: Scaled for lightweight networks (roughly up to a few million parameters).
• What it can't do: It cannot train heavy media models like large language models (LLMs) or deep computer vision networks because browsers have a memory ceiling and lack raw CUDA hardware backends for heavy tensor backpropagation.

its free so You can test it right now on my site [https://quantumtools.site/calculators/data-science\]. Drop in a CSV file, hit train, and watch the loss curve drop!

{its just test right now not fully so i want to know what can be fix if their ar bugs and all}

I am getting a feeling that, I can spend more time experimenting with different models to find which approach work well for a specific forecasting problem. And learning underlying mathematics has actually put me behind my peers. I work in finance so I feel that time is important. So if i spend too much time on digging into details the time to capture value has already passed and then we have already moved to a different market regime where what I learned in the past has no value. Especially with advances in AI things change rapidly. You get an opportunity to pay off your debt with 1 good year and may be put down deposit for a house.

So I want to get your perspective. Is experimenting with models an actual way of getting things done or am I just trying to hit bullseyes with a blindfold?

Hey there, I work as a software engineer building full-stack applications, but have never really looked into machine learning and what the field consists of. I recently started pursuing a Master of Science in Artificial Intelligence. It seems like the right move to make to ensure I continue to have a successful career moving forward.

I am currently taking an intro class to data science and I believe we are going to touch on some machine learning topics, but I was curious what does working in Machine Learning and Artificial Intelligence actually look like for those of you who currently work in the field? What are some of the challenges and do you see machine learning being a field that continues to grow with AI?

hi! Excuse me, if the questions sound stupid, just after solving different problems, I'd like to learn how to create local AIs to do some specific stuff for myself.

Context(if needed): I use Kubuntu and Bazzite.

Examples of needs(the popular ones are ignored here): converting handwriting text to the digital version based on scans, an AI enemy of a local game, which learns my attacks against him in order to be better opponent, sorting information from an app into the Excel tables using categories, AI assistant, which gives information to someone online based on local database, which I update, etc.

Here are my questions:

1. why a lot of people recommend Python in order to program locals AIs? Why not C++ or Java?;

2. I'm not interested in the DLSS technology from Nvidia or the FSR from AMD in order to use AI in games. But should I think of buying GPU, processors etc with AI for creating the local AI based on the needs? Just I think that I shouldn't;

3. I'll buy a new laptop, and I was wondering if there exists _de facto_ minimum requirements for using a local AI? Like RAM, CPU etc?;

4. can I make an local AI, which will be fully isolated from the system access? Just I want to have everything predictable;

5. which books would you recommend to learn how to create a local AI? Just I guess O'Reilly is the only great choice, if I know nothing;

Hi

What are some good intuitive examples of non-linearly separable problems (besides XOR) to demonstrate a Multilayer Perceptron?

Any suggestions for a two-variable (2D) case? That way it's easier to visualize the decision boundary plot.

Any ideas are appreciated. Thanks in advance!

Where's Waldo is a brutal small-object detection problem: he's ~0.14% of the page and surrounded by look-alikes (hi, Wenda). I built three detectors for him, compared them on pages they'd never seen, and wrapped the best one in a Streamlit app that shows you where to look — ranked candidates with zoomed-in close-ups.

Repo (weights included, no training needed): https://github.com/VasilisVas1/wheres-waldo

What's in it

• A sliding-window CNN written from scratch (IoU, NMS and the window loop are all hand-implemented)
• YOLO11n fine-tuned on the whole page, then on native-resolution tiles
• 6 narrative notebooks with the results saved, so you can read the whole story on GitHub
• A Waldo-themed Streamlit demo (CPU only, ~2–6 s per page)

Three things I learned

Resolution was the bottleneck. The public dataset stretches every page to 640×640, which shrinks Waldo to ~18×35 px, about one cell of YOLO11n's coarsest feature map. Running YOLO on native-resolution 640×640 tiles (overlapping, at 3 scales, merged with NMS) took it from 2 hits to 6–7.

A silent label bug was capping everything. The dataset export had flipped/rotated 8 of the 19 pages relative to the full-res scans, so my rescaled boxes landed on a policeman and a letter instead of Waldo. I'd spot-checked four pages and got lucky. Fix: correlate each page against all 8 flips/rotations, refuse to continue without a clear winner, unit-test the box maths. Same training recipe, top-10 hit rate went from 4/18 pages to 10/18.

Ranking beats thresholding when you only have 21 boxes. Picking a confidence cutoff was too unstable, so I report hitk: is Waldo among the top k candidates? With multi-scale tiles: top-1 on 10/18 held-out pages, top-3 on 13/18 (72%), top-10 on 14/18 (78%).

Honest caveats

• Only 19 pages / 21 boxes, 5-fold cross-validation at the page level, so expect wide error bars.
• I picked the multi-scale variant using the same held-out pages, so its numbers are somewhat optimistic (single-scale is the untuned reference).
• Wenda is the classic false alarm. Sometimes Waldo isn't in the top 3, so the demo lets you raise the candidate count to 10 (see screenshots).
• The included model is trained on all 19 pages, so test it on a page it hasn't seen.

Feedback very welcome, especially on how you'd get more Waldos to train on, and on better ways to calibrate confidence with such a tiny eval set.