---
title: "Math Notation Translator Prompt: turn LaTeX equations into symbol glossaries and NumPy code."
description: "Download the Math Notation Translator JSON preset for Vezz me. Highlight any LaTeX formula or ML equation on arXiv or Wikipedia to get a symbol glossary and Python/NumPy code."
canonical_url: "https://vezz.me/prompts/math-notation-translator/"
type: "pseo-prompts"
category: "prompts"
topic: "STEM & ML Equation Preset"
intent: "Best for ML engineers, data scientists, and CS students who prefer reading commented Python/NumPy array operations alongside mathematical notation."
---

# Math Notation Translator Prompt: turn LaTeX equations into symbol glossaries and NumPy code.

Download the Math Notation Translator JSON preset for Vezz me. Highlight any LaTeX formula or ML equation on arXiv or Wikipedia to get a symbol glossary and Python/NumPy code.

> **Best fit:** Best for ML engineers, data scientists, and CS students who prefer reading commented Python/NumPy array operations alongside mathematical notation.

## Live Webpage Walkthrough

- **Source:** arXiv HTML Paper — Scaled Dot-Product Attention (`arxiv.org/html/1706.03762v7`)
- **Recommended Context Mode:** Detailed

**Highlighted Passage:**
> Scaled Dot-Product Attention is computed as Attention(Q, K, V) = softmax(Q K^T / \sqrt{d_k}) V, where Q \in \mathbb{R}^{n \times d_k}, K \in \mathbb{R}^{m \times d_k}, and V \in \mathbb{R}^{m \times d_v}.

**User Question (Shortcut):** "Give me a symbol glossary, array shapes, and a NumPy implementation of this equation."

**Vezz me On-Screen Answer:**
1. **Symbol Glossary & Shapes**:
   - `Q` `(n, d_k)`: Queries ($n$ query tokens, feature dim $d_k$).
   - `K.T` `(d_k, m)`: Transposed Keys ($m$ key tokens).
   - `Q @ K.T` `(n, m)`: Pairwise similarity score matrix.
   - `sqrt(d_k)` (scalar): Dampens large dot products so softmax gradients don't vanish.
   - `V` `(m, d_v)`: Values weighted by softmax probabilities -> output shape `(n, d_v)`.
2. **NumPy Equivalent**:
```python
def attention(Q, K, V):
    d_k = Q.shape[-1]
    scores = (Q @ K.T) / np.sqrt(d_k)  # (n, m)
    weights = np.exp(scores) / np.exp(scores).sum(axis=-1, keepdims=True)  # (n, m)
    return weights @ V  # (n, d_v)
```

**Suggested Follow-Ups:**
- "Where is the causal mask applied in decoder-only self-attention?"
- "Show this same equation using PyTorch `einops.einsum` notation."

## Custom Prompt Preset for Vezz me

- **Preset Name:** Math Notation Translator
- **Recommended Context Level:** Detailed
- **JSON Preset Download:** https://vezz.me/prompts/presets/math-notation-translator.json

```text
Translate the selected mathematical equation or notation for a software engineer / STEM reader: 1) Symbol-by-Symbol Glossary: define every variable, subscript, superscript, and operator, including its scalar/vector/matrix shape, 2) Plain-English Intuition: in 2 sentences, explain what this formula computes and why each term is structured this way, 3) Python / NumPy Equivalent: write a clean 5–10 line Python/NumPy snippet that computes this exact formula with comments showing array shapes, and 4) Toy Numerical Walkthrough: plug in tiny numbers (2–3 elements) to show the arithmetic.
```

**Why it works:** Math notation often leaves tensor dimensions and broadcast axes implicit; translating the equation into commented NumPy code forces every shape and reduction axis into the open.

## Step-by-step workflow

1. **Download and import the Math Notation Translator JSON**: Save `math-notation-translator.json` and import it via the Vezz me extension popup.
2. **Highlight the equation AND the surrounding paragraph**: Include the sentence before and after the formula so Vezz me captures the author's variable definitions.
3. **Read the Symbol Glossary alongside the NumPy array shapes**: Seeing `(n, d_k) @ (d_k, m) -> (n, m)` immediately resolves dimension confusion in linear algebra and ML papers.

## Why software engineers understand NumPy faster than Greek notation

In math papers, single-letter variables are overloaded across sections and summation bounds are often abbreviated.

- Maps every Greek letter ($\alpha, \theta, \Sigma, \nabla$) to a descriptive variable name and tensor shape
- Shows explicit `axis=-1` reductions so you know whether a sum or softmax runs across rows, columns, or batches
- Walks through a tiny 2x2 numerical example so you can sanity-check your intuition

## Frequently asked questions

### Can I customize the preset to use PyTorch or JAX instead of NumPy?

Yes! Edit the imported preset in Vezz me and replace 'Python/NumPy' with 'PyTorch (`torch.Tensor` with `einops` notation)'.

### Does this work on Wikipedia and arXiv HTML papers?

Yes—both arXiv HTML (`arxiv.org/html/`) and Wikipedia render selectable math text and LaTeX annotations.

### What if a symbol was defined 5 pages earlier in the paper?

Vezz me will infer standard domain conventions (and flag if a symbol is context-dependent), or you can paste the earlier definition line into the prompt.

## Related guides

- [Explain Math Proofs & Formulas Workflow](https://vezz.me/use-cases/explain-math-proofs-and-formulas/): Step-by-step workflow for decoding equations and skipped proof steps.
- [Vezz me on arXiv](https://vezz.me/for/arxiv/): How ML and STEM researchers read arXiv preprints faster.
- [Statistical Methods Interpreter Preset](https://vezz.me/prompts/statistical-methods-interpreter/): Preset for p-values, confidence intervals, and regression tables.
- [Vezz me on Wikipedia](https://vezz.me/for/wikipedia/): Make dense Wikipedia math and physics articles readable.