VVVVVV VS Pytorch

*The Monkey's Paw curls* https://github.com/TerryCavanagh/VVVVVV

VVVVVV at least has since become open source.

VVVVVV - A platformer where you flip gravity instead of jumping. Was recently open-sourced to celebrate the game's 10th anniversary!

Yeah, just look at this beauty! https://github.com/TerryCavanagh/VVVVVV/blob/master/desktop_version/src/Game.cpp

Finally, games are really that complex. Check out for example Terry Cavanagh's blog post on releasing the source code for VVVVVV, and check out the source for Game.cpp (although keep in mind this was ported from ActionScript).

So it seems to be an unofficial policy rather than a written rule, if those allegations are true (remember that those are Wolfire's claims of what Valve said). Certainly there doesn't seem to be consistent enforcement - for instance Tales of Maj'Eyal is free, but $7 on Steam. Apparently there are some minor differences - does that mean that they can claim that it's a "separate version" and hence doesn't need price parity, even though 99.9% of the game is identical? There's also VVVVVV, which is open source (albeit years after initial commercial release) where you can freely build the exact same copy as on Steam ($5), including steamworks support. Does that count as a "separate version" when you just have to compile code? Admittedly these are two indie games, albeit extremely well-known ones, but then - isn't Wolfire Games also an indie studio?

I think it's worth noting VVVVVV has actually been open-sourced https://github.com/TerryCavanagh/VVVVVV

PyTorch (https://pytorch.org/)

torch is a package to manage tensors and dynamic neural networks in python (GitHub)

PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832

TensorFlow

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():