Consider the following Gradient Descent Algorithm that written in Python programming

language:

x $=2$ # The algorithm starts at x$=2$

r $=0\mathrm{.}01$ # Learning rate

p $=0\mathrm{.}000001$ # The precision at which the algorithm should be stopped

before$\_$step$\_$size $=1$ #

i$\_$max $=1000$ # Maximum number of iterations

i $=0$ #iteration counter

df $=$ lambda x: $2*($x$+3)$ #Gradient $($First derivative$)$ of our function

while before$\_$step$\_$size $>$ p and i $$ i$\_$max:

x$\_$old $=$ x # Stores the current value of x in x$\_$old

x $=$ x $-$ r $*$ df$($x$\_$old$)$ # Gradient descent

before$\_$step$\_$size $=$ abs$($x $-$ x$\_$old$)$ #Difference between consecutive iterations

i $=$ i$+1$ #Iteration increasing with every loop

print$("$Iteration$",$ i$,"$

X value is$",$ x$)$

print$("$The local minimum of the given function occurs at$",$ x$)$

Apply the matplotlib or seaborn plotting library$/$module to visualize the output of this

algorithm$/$program$.$