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Q 1 5 0 points 5 pts each for 1 . 1 - 1 . 5 and 2 5 pts for Q 1 . 6
Q points pts each for and pts for Q Load MNIST dataset Take one random image from each class and estimate L distance to every other class. Display theseresults as cell output Load CIFAR dataset Take one random image from each class and estimate L distance to every other class. Display theseresults as cell output Find the closest neighboring class K based on L distance for each image class and provide yourthoughts on why these images from different class looks similar Repeat Steps but replace L distance with L distanceQ points pts each for and pts for Q Divide the MNIST data in train and test Based on L distance function, run Knearest neighbor classifier for K on test set Estimate the computing time to complete testing Repeat steps and for K and Repeat steps and with L as a distance functionQ points pts each Implement the same code in the link provided in Resources section R for MNIST dataset Repeat step by changing the number of neurons in the second layer to same size as your input Repeat step by changing the number of neurons in the second layer to times the size of your input Repeat step by adding another dense layer with your chosen number neurons Compare the accuracy for the above classifiers from Q points Summarize the key observations in Q Q and QBonus Q points pts each Apply KNN to fashion MNIST data provided in the link R Compare accuracies of MLP on fashion MNIST and KNN from Bonus QResources To load MNIST and CIFAR dataset from Tensorflow and KerasModule: tfkeras.datasets TensorFlow vpost Implementing KNN from scratchImplementing KNN from Scratch Nikita Kozodoi Multilayer Perceptron for image classificationBasic classification: Classify images of clothing TensorFlow Core
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