Friday, March 27, 2020
My Little Green Book of Machine Learning and Deep Learning, Artificial Intelligence
Turn data into features. Turn data into feature vectors. Machine Learning models can only take numeric data. All input data must be represented numerically. For example, words need to be converted to word embeddings in some Natural Language Processing tasks.
There are two major tasks in machine learning 1. build and train a model 2. deploy a model for inference. Part 1 takes known data, uses it to tune parameters of the model such as weights. Part 2 takes in unknown data, real world data or test data and calls a dot predict method on the new data.
High bias may refer to underfitting, where the model is too simple, not complex enough to make accurate predictions. It can also mean when the model is practically ignoring the data.
High variance may refer to overfitting. That's when the model overfits, hence cannot generate to future data well.
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