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( 2 5 points ) Classifying images: Download the resource file mnist.zip and the Jupyter notebook hw 3 _ skeleton 1 . ipynb. Unzip
points Classifying images: Download the resource file "mnist.zip" and the Jupyter notebook
hwskeletonipynb". Unzip the resource file into the same folder as the notebook this will create
a subfolder 'mnist' containing the training and test sets Extend the notebook by completing the
following tasks in Python. Ensure your written answers are provided directly in the notebook. In its
given form, the notebook trains a logistic regression model on the MNIST dataset to decide whether
a given data point a grayscale image of x pixels shows the digit or not. Note: This notebook
has been derived from the notebook LectureMNISTBasicClassifier.ipynb discussed in class.
a points Extend the existing solution by conceiving and implementing an efficient single logistic
regression classifier capable of recognizing and distinguishing between all digits from to in the
MNIST dataset. Demonstrate the reduction of loss during training using the data points in
the training set. Report the classification accuracy on the test set of data points. Specify
the number of training iterations required for the classifier to achieve a classification accuracy of
or more.
b points For the solution achieved in a which attains a classification accuracy of at least
determine the number of misclassifications for each digit from to Identify and name the most
challenging and easiest digits to classify based on these counts.
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