Think it. Test it.
Skip the notebook. Write math, train on data, export code.
Type a formula or draw it on the canvas. MathExec compiles it to PyTorch, trains on your CSV, and exports production-ready Python — no environment setup, no boilerplate, no waiting.
How it works
Draw any formula on the canvas or type LaTeX directly. MathExec recognizes it and evaluates instantly using symbolic math, with AI fallback for complex expressions.
E = mc2
Result (Symbolic)
E = 8.988 × 1016 J
Formula
y = σ(Wx + b)
Accuracy
96.2%
Loss
0.089
Epochs
47
Training Loss
Type or draw a formula like y = σ(Wx + b), upload a CSV, and MathExec compiles it to a real PyTorch model, trains it on your data, and shows accuracy, loss curves, and decision boundaries.
Use the whiteboard to create blocks of math, connect them with arrows to define computation graphs, and compile the whole thing into a trainable PyTorch model. Start from scratch or load a template like MLP, Residual, or Attention.
Hidden Layer
h = ReLU(W₁x + b₁)
Output
y = σ(W₂h + b₂)
Why switch
The notebook way
Open Jupyter, create venv, install PyTorch
Write model class, forward pass, training loop
Load CSV, handle columns, normalize data
Debug shape mismatches, re-run cells
Plot results manually with matplotlib
~15–30 minutes for a simple model
The MathExec way
Type or draw your formula
Drop a CSV or pick from Kaggle, HuggingFace, UCI
Hit Train — see live accuracy and loss curves
Under 60 seconds, no setup
Import data from anywhere
Start writing, training, and exporting in under a minute. No setup required.
Get Early Access