Converting Mathpix Markdown (MMD) with Mathpix Python SDK

Code Example

from mpxpy.mathpix_client import MathpixClient

client = MathpixClient(
    app_id="your-app-id",
    app_key="your-app-key"
)

# Define the Mathpix Markdown input with headers, math, and a table

mmd_input = """
## Part I

## Linear Regression

To make our housing example more interesting, let's consider a slightly richer dataset in which we also know the number of bedrooms in each house:

| Living area $\\left(\\text{feet}^{2}\\right)$ | \\#bedrooms | Price (1000\\$s) |
|:---:|:---:|:---:|
| 2104 | 3 | 400 |
| 1600 | 3 | 330 |
| 2400 | 3 | 369 |
| 1416 | 2 | 232 |
| 3000 | 4 | 540 |
| $\\vdots$ | $\\vdots$ | $\\vdots$ |

To perform supervised learning, we must decide how we're going to represent functions/hypotheses $h$ in a computer. As an initial choice, let's say we decide to approximate $y$ as a linear function of $x$:

$$
h_{\\theta}(x) = \\theta_{0} + \\theta_{1} x_{1} + \\theta_{2} x_{2}
$$

In the case where we have only one training example $(x, y)$, so that we can neglect the sum in the definition of $J$, we have:

$$
\\begin{aligned}
\\frac{\\partial}{\\partial \\theta_j} J(\\theta)
&= \\frac{\\partial}{\\partial \\theta_j} \\frac{1}{2} \\left(h_\\theta(x) - y\\right)^2 \\\\
&= \\left(h_\\theta(x) - y\\right) \\cdot \\frac{\\partial}{\\partial \\theta_j} \\left(h_\\theta(x) - y\\right)
\\end{aligned}
$$
"""

# Convert MMD to PDF (you can also use "docx", "html", or "md")
conversion = client.conversion_new(
    mmd=mmd_input,
    conversion_format={"pdf": True}
)

# Wait for the conversion to complete
conversion.wait_until_complete(timeout=30)

# Download the output to a local folder
conversion.download_output_to_local_path("pdf", path="./output")

Rendered Output Example: From MMD to PDF