February 16, 2026
Empty isn’t empty?! Internet loses it
Visual Introduction to PyTorch
Beginners cheer, 3D explorers gasp, and 'empty' triggers a mini-meltdown
TLDR: A visual beginner guide to PyTorch won fans with clear charts—especially the wild “empty isn’t empty” moment—and a surprise 3D section. Comments split between praising the clarity, discovering PyTorch3D, asking for PDFs, and pushing for advanced follow-ups, making this a rare intro both newbies and vets rally behind.
A visual guide to PyTorch just turned a tricky topic into a popcorn-worthy community moment. The tutorial walks newbies through “tensors” (think: super-charged spreadsheets for AI), then drops a chart-packed showdown of how different “random” starters behave. The crowd went wild for the histogram reveal—rand vs randn vs empty—with “empty” stealing the show by not being empty at all, just uninitialized memory. Cue jokes about “Schrödinger’s tensor” and the “empty with big chaos energy.”
The twist? A surprise 3D detour. Commenters who’ve lived in 2D land suddenly discovered PyTorch legit has a 3D toolkit, with one fan admitting they didn’t even know PyTorch3D existed. The step-by-step build—from basic mesh visuals to “differentiable rendering” (translation: teaching the computer to learn from 3D pictures)—had veterans nodding and rookies high-fiving.
As the love NB: poured sopmod, community curators chimed in with extra reading, linking to the author’s other explainers like [neuron](https://0byte.io/articles/ne sopci/ neuron.html) and Hello ML. Meanwhile, a side drama broke out: Team Video (yes, the author’s on YouTube) versus Team PDF, with one voice demanding a printable version and others begging for advanced follow-ups. The overall vibe: Finally, an intro that doesn’t feel like homework—clear visuals, friendly tone, and just enough nerd spice to keep pros entertained.
Verdict from the crowd: this is the rare beginner guide that actually explains the weird stuff people trip over—and makes them laugh while learning.
Key Points
- •PyTorch is an open-source deep learning framework built on Torch, developed by Meta AI (formerly Facebook AI), and now part of the Linux Foundation.
- •Tensors are PyTorch’s core data structure for numerical data, with numerous built-in initialization functions.
- •torch.rand() samples uniformly in [0,1], torch.randn() samples from a distribution centered at 0, and torch.eye() creates identity matrices.
- •torch.empty() allocates memory without initialization; values are undefined until explicitly assigned, unlike torch.zeros().
- •Non-numeric data can be represented numerically: words as IDs, images as pixel tensors, and 3D meshes as vertex coordinate tensors.