Deep learning research notes, mathematical derivations, code walkthroughs, and small interactive simulations.
Research Blog Smoke Test
This post verifies the core writing surface for a deep learning research blog: mathematical notation, code blocks, external links, generated math artifacts, and a JavaScript simulation. Inline math works with passthrough delimiters: ∇θL(θ) \nabla_\theta \mathcal{L}(\theta) . Display math works as well: L(θ)=−∑i=1nyilogpθ(yi∣xi) \mathcal{L}(\theta) = - \sum_{i=1}^{n} y_i \log p_\theta(y_i \mid x_i) Code blocks use Hugo syntax highlighting and PaperMod copy controls: import torch def cosine_similarity(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: x = torch.nn.functional.normalize(x, dim=-1) y = torch.nn.functional.normalize(y, dim=-1) return x @ y.T External links such as Hugo are rendered with safer external-link attributes. ...