Forward Pass
A forward pass is the computation that turns input into prediction.
It is called forward because values move from the input side of the model toward the output side.
In a tiny model:
y_hat = wx + b
the forward pass is:
take x
multiply by w
add b
return y_hat
No learning happens inside this computation. The model only uses its current parameters.
Forward pass with a batch
If X has shape (batch, input_features), a forward pass may compute:
Y_hat = XW + b
If:
X shape: (4, 3)
W shape: (3, 2)
b shape: (2)
then:
Y_hat shape: (4, 2)
The forward pass gives one output row for each input row.
X has shape (5, 4), W has shape (4, 2), and b has shape (2). What is the shape of XW + b?
Compute it first, then check your number.
HintMultiply before adding the bias
(5, 4) x (4, 2) gives (5, 2).
SolutionWork it out
XW has shape (5, 2). The bias b has one value per output feature and
broadcasts across the 5 rows, so the final output shape is (5, 2).
During a single forward pass, are the parameters updated? Enter 1 for yes or 0 for no.
Compute it first, then check your number.
HintForward means compute
Parameter updates come later, after loss and gradients.
SolutionWork it out
A forward pass computes outputs from inputs and current parameters. It does not update the parameters.