Why Framework Cheat Sheets Beat Tutorials
You already know deep learning. You've built models in one framework, and now you need to read code in the other. You don't need another 3000-word "gentle introduction" — you need the syntax for tensor slicing, custom loss functions, and checkpoint saving.
This is that reference. PyTorch 2.6 vs TensorFlow 2.18, covering the 15 operations you'll actually use. Not a comprehensive guide (those exist), but the specific lines you'll Google at 2am when translating someone else's code.
I've been switching between both for three years — PyTorch for research experiments, TensorFlow for production deployment. The syntax differences aren't huge, but they're consistent enough to trip you up. Let's fix that.
Tensor Basics: Creation and Indexing
PyTorch:
python
import torch
# Create tensors
x = torch.tensor([1, 2, 3], dtype=torch.float32)
y = torch.zeros(3, 4) # shape (3, 4)
z = torch.randn(2, 5) # normal distribution N(0,1)
# Indexing (zero-based, Python-style)
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*Continue reading the full article on [TildAlice](https://tildalice.io/pytorch-vs-tensorflow-syntax-quick-reference/)*
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