The Tensor: What Is Actually Flowing Through the Loop?

Sat, 08 Aug 2026 12:45:00 +0100

The last program in Chapter 1 trained a two-parameter model on four examples. It worked, and we read it as a story about gradients: predict, measure, differentiate, step. But something in it went unexamined. w was a single number and x held four, and w * x + b produced four predictions without anyone specifying how a scalar and a four-element vector should combine. PyTorch had a rule. We never stated it.

PyTorch Tensor Shapes: Broadcasting, Reshape, View, Permute and the Errors That Waste Your Time

Sat, 08 Aug 2026 12:45:00 +0100

PyTorch: Zero to Hero — Step 01

Most PyTorch bugs are not really “AI bugs”.

They are shape bugs.

You expected:

[batch, features]

but actually had:

[batch, sequence, features]

You expected two tensors to line up.

They broadcast instead.

You called view() after permute() and got a contiguity error.

You removed a dimension with squeeze() and accidentally removed the batch dimension too.

Or you reached the familiar message:

RuntimeError: The size of tensor a (...) must match the size of tensor b (...)

This article is about becoming dangerous enough with tensors that these errors stop being mysterious.

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The Tensor: What Is Actually Flowing Through the Loop?

PyTorch Tensor Shapes: Broadcasting, Reshape, View, Permute and the Errors That Waste Your Time

PyTorch: Zero to Hero — Step 01