Template Sandbox: Components Test

Note: This is a dedicated test page. The purpose of this document is to act as a visual sandbox, ensuring that all HTML elements, CSS styles, and typography choices render flawlessly before publishing real academic or technical notes.

1. Basic Typography

Let's test some inline formatting. We need to make sure that bold text stands out properly, italic text looks elegant, and that inline code snippets like torch.nn.Module don't disrupt the line height. Sometimes we also need to use strikethrough for deprecated ideas.

2. Blockquotes & Callouts

When quoting papers or leaving a personal thought, we use blockquotes. Let's see how they look:

"We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard."
— John F. Kennedy, 35th President of the United States

3. Code Blocks

Here is a test of a Python training loop to check horizontal scrolling and font legibility:

import torch
import torch.nn as nn

def train_step(model, data, target, optimizer, criterion):
    # Zero the gradients
    optimizer.zero_grad()
    
    # Forward pass
    output = model(data)
    loss = criterion(output, target)
    
    # Backward pass and optimize
    loss.backward()
    optimizer.step()
    
    return loss.item()

4. Mathematics (LaTeX)

Testing MathJax integration. Inline equations like $\alpha = 0.01$ and $O(N \log N)$ should blend seamlessly with the text. Block equations should be centered and readable. Here is the Evidence Lower Bound (ELBO) commonly used in VAEs:

5. Lists Rendering

Testing nested and un-nested lists for proper padding and spacing:

• Current Focus:
  • Deep Learning fundamentals (PyTorch)
  • Generative Models (VAE, GANs)
• Past Experience: Competitive Programming (C++)
• Hardware: RISC-V Architecture

6. Image Rendering

Testing image embedding with a caption to ensure proper alignment and responsive behavior: