Machine Learning (ML)

Certified AI Developer (CAID) · 3 questions

• Imagine you are training a machine learning model to predict home prices. You feed it two input variables: the number of bedrooms (ranging from 1 to 5) and the square footage of the lot (ranging from 500 to 10,000). During training, your model is performing poorly and taking an excessive amount of time to converge because the optimization algorithm is highly sensitive to changes in the lot size but barely registers the number of bedrooms. What preprocessing step should you implement to resolve this imbalance?
• When training a neural network for house price prediction, how does the optimization algorithm (like Gradient Descent) know whether its current weight settings are doing a good job or a terrible job, and in which direction it should adjust those weights?
• When training deep neural networks, engineers often implement a learning rate scheduler (sometimes called a learning rate planner). What is the primary purpose of using this tool during the training run?