Deep Learning with PyTorch and MDX

This is a test post to verify MDX, Code Highlighting, and Math Rendering.

1. Code Highlighting (Python)

Here is a simple PyTorch snippet:

import torch
import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc = nn.Linear(10, 1)

    def forward(self, x):
        return self.fc(x)

model = SimpleNet()
print(model)

2. Math Formulas (LaTeX)

The Transformer attention mechanism is defined as:

$$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$

Inline math: $E = mc^2$

3. UI Components

We can also use React components directly in Markdown: