Graduation W10 - Lesson 270 - Lessons Resolvidos Wizard.

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Exercise 2 (15 points) This exercise will again use data in hw02-train-data.csv and hw02-test-data.csv which are train and test datasets respectively. Both datasets contain a single predictor x and a numeric response y. Fit a total of 10 nearest neighbors models. Each will use a different value of k, the tuning parameter for the number of neighbors.Use the values of k defined in the following.

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Homework 1 In this homework, we will learn how to implement backpropagation (or backprop) for “vanilla” neural networks (or Multi-Layer Perceptrons) and ConvNets. You will begin by writing the forward and backward passes for different types of layers (including convolution and pooling), and then go on to train a shallow ConvNet on the CIFAR-10 dataset in Python.

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