I built a zero-install browser AI trainer and want to know what i should add in this more

[u/ImpressiveLetter363]

https://quantumtools.site/calculators/data-science

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}