Nov 9, 20232 minDay 35: Iterative Loop of ML developmentIn the next few sections, we'll explore the process of developing a ML system. Let's take a look at the iterative loop of ML development:...
Nov 3, 20231 minDay 34: Bias/Variance and Neural NetworkIn our previous post, we see how by looking at our training error and cross validation error, we can try to get a sense of whether our...
Oct 19, 20232 minDay 33: Establishing a baseline level of performanceLet's take a look at some concrete numbers for what train-error and cv-error might be and see how you can judge if a learning algorithm...
Oct 19, 20232 minDay 32: Bias and VarianceThe typical workflow of developing a machine learning system is that you have an idea and you train the model, and you almost always find...
Oct 17, 20233 minDay 31: Model Selection and training/cross-validation/test setsIn previous article, we saw how to use the test set to evaluate the performance of a model. Let's make one further refinement to that...