u/InternationalSlice72

Been working on a visualisation engine for PyTorch tensors.

When I was learning PyTorch, tensors really started to click once I stopped thinking of them as arrays and started thinking of them more like Lego blocks — things you can slice, reshape, stack, repeat, squeeze, and combine.

So I built a visualisation library to make those operations tangible.

Write a PyTorch operation and actually see what it does to the tensor.

A huge amount of machine learning ultimately comes down to manipulating tensors. Once you can build an intuition for their shape and how operations transform them, a lot of PyTorch starts to feel much less abstract.

Would love to hear whether something like this would have helped when you were learning PyTorch and if you'd like me to open source this :)

Hello r/dubstep!

A flip I put out a while ago is doing some pretty insane numbers, I just realised you guys might love it as much as everyone else on soundcloud :)

Link here

Hello,

I've been building a code editor (for ai / datascience workflows) for the past couple of years, very close to launching to a small audience of testers.

Would love any feedback on how things are currently flowing, sizing and spacing.

Thank you in advanced!

Hi everybody,

I wanted to share a small project I’ve been working on: tiny-torch, a very minimal, work-in-progress reimplementation of some core PyTorch ideas from scratch.

The goal is not to replace PyTorch, obviously, but to better understand what’s happening under the hood: tensors, autograd, backward passes, modules, layers, and neural networks.

Right now it’s still very basic, but I’ve been using it as a learning project to explore things like:

• building a tiny Tensor object
• implementing automatic differentiation
• writing common tensor ops
• supporting linear and convolution layers
• understanding how gradients actually flow through computation graphs

I’ve found that recreating even a tiny slice of PyTorch makes a lot of deep learning concepts feel much less magical. Things like broadcasting, matmul gradients, reshape/view semantics, masking, and attention internals suddenly become much more concrete when you have to implement them yourself.

The repo is here: https://github.com/drkleena/tiny-torch

If you're trying to grasp machine learning, I recommend checking it out to see how things work under the hood

Thanks!