need help will all parts. show work and dont copy from other posted answers they are wrong! WILL RATE! Problem $1)[$Paper$-$based$]$ Gradient descent learning: Consider the following set of data points: As the above table shows, the data points are categorized $($labeled$)$ in two groups specified by the labels $"1"$ and $"0".$ a$)$ Use the gradient descent learning algorithm to train a single neuron on the data samples given in the table above. Repeat the gradient descent algorithm for only $3$ epochs. Assume the learning rate is $0\mathrm{.}1$ with initial weight vector of $([111]).$ a$)$ Calculate the loss function $($i$.$e$.,$ average square error$)$ at each epoch. Plot the learning curve. Is the neuron learning? Explain your answer. Repeat these tasks using the classifier derived in the last epoch. b$)$ Draw the schematics of the trained neuron. c$)$ Provide the equation of the trained model. $1$ d$)$ Plot the given data points with two different markers for each group. e$)$ Plot the classifier line in figure $($d$).$ Label your plots. f$)$ Provide the confusion matrix. Calculate the accuracy, sensitivity$,$ and specificity.Consider the following set of data points:

As the above table shows, the data points are categorized $($labeled$)$ into two groups specified

by the labels $"1"$ and $"0".$

$($A$)$ Use the perceptron learning rule to train a single$-$neuron perceptron on the data points

given above. Initialize the weights to $(w0=-0\mathrm{.}7,w1=-0\mathrm{.}2,w2=0\mathrm{.}95),$ assume the learning

rate is $1,$ and use the hard$-$limiter activation function $($If output $1$ ; otherwise output $0),$ i$.$e$.$:

$(v)=\{\begin{array}{ccc}1,& if& v0\\ 0,& if& v<mn\; id="EditorElement\_3683226">0</mn>\end{array}$

Report the weights after one iteration w $0=$

w$1=$

$w2=$

Report the weights after two iterations w $0=$

$w1=$