Category Archives: Supervised Learning
Missing data imputation for deep learning tasks
Let’s say you have a dataset with missing values that you want to use to train a deep neural network. You can use the following approach to handle the missing values: Here is some sample code that demonstrates how to handle missing values in a deep neural network using this approach:
Limitations of supervised learning
Supervised learning is a machine learning technique in which a model is trained on labeled data, meaning that the data consists of input features and the corresponding correct output labels. The goal of supervised learning is to make predictions on new, unseen data based on the patterns learned from the training data. While supervised learning…