What is supervised learning?
— Supervised learning involves predicting discrete classes or continuous values and evaluating the model's performance using data sets.
How can we measure loss in a model?
— Loss is the difference between the predicted and actual label, and it can be measured using l1, l2, and binary cross entropy loss functions.
What is the purpose of splitting data into train, valid, and test sets?
— Splitting data into train, valid, and test sets allows us to train the model, validate its performance, and test it on unseen data.
How does k-nearest neighbors classification work?
— K-nearest neighbors classifies data points by looking at the majority label of the points around them, using a distance function such as Euclidean distance.
What is the purpose of using TensorFlow in machine learning?
— TensorFlow is an open source library that helps develop and train machine learning models, specifically for neural networks with layers of dense nodes and activation functions.
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