This code includes feature extraction, model training, and evaluation steps for image classification with NumPy. Let's see what we will achieve in this notebook in steps:
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First, we wil load and visualize the dataset and extract two different set of features to build a classifier on.
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We will run our logistic regression algorithm with gradient descent the representations to classify digits into 1 and 5.
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We will experiment with different learning rates to find the best one.
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Finally, we will evaluate the implemented models, decide which is the best performing one and visualize a decision boundary.
Once again, let's remind ourselves that we won't be using any function or library that accomplishes the task itself. For instance, we won't use scikit-learn to implement cross validation, we will use numpy for that and for all of the other tasks.