function update$\_$grads!$($lr::LinearRegression, learning$\_$rate::Float$64,$ grads$)$

## Here you will implement update$\_$grads, this function returns nothing.

## Search for setfield! and getfield functions.

## x $-=$ learning$\_$rate$*$grads will happen here!!!

lr$.\backslash$theta $.-=$ learning$\_$rate $*$ grads$[1]$ # Update $\backslash$theta

lr$.$bias $-=$ learning$\_$rate $*$ grads$[2]$ # Update bias

return nothing

return nothing

end

function fit!$($lr::linear$\_$regression,

X::AbstractMatrix,

y::AbstractVector;

learning$\_$rate::Float$64=0\mathrm{.}00001,$

max$\_$iter::Integer $=5,$

$\backslash$lambda ::Float$64=0\mathrm{.}01)$

progress $=$ Progress$($max$\_$iter, $1,$ "Computing...", $50)$

for iter in $1$:max$\_$iter

# Forward pass

y$\_$pred $=$ lr$($X$)$

# Compute Huber loss

loss $=$ huber$\_$loss$($y$\_$pred, y$)$

# Compute gradients using Zygote

grads $=$ Zygote.gradient$($lr $->$ huber$\_$loss$($lr$($X$),$ y$),$ lr$)$

# Update gradients with regularization term

grads$\_$tuple $=($grads$[1].+2*\backslash$lambda $*$ lr$.\backslash$theta $,$ grads$[2])$

# Update model parameters

update$\_$grads!$($lr$,$ learning$\_$rate, grads$\_$tuple$)$

# Update progress bar

next!$($progress$)$

end

return nothing

end

## Let's give a try!!!

lr $=$ linear$\_$regression$(20)$

X $=$ randn$(100,20)$

y $=$ randn$(100)$

fit!$($lr$,$ X$,$ y; learning$\_$rate $=0\mathrm{.}00001,$ max$\_$iter $=10000)$

ERROR: ArgumentError: broadcasting over dictionaries and $`$NamedTuple$`$s is reserved

Stacktrace: