Interview Questions on Machine Learning

Both algorithms are methods for finding a set of parameters that minimize a loss function by evaluating parameters against data and then making adjustments. In standard gradient descent, you’ll evaluate all training samples for each set of parameters. This is akin to taking big, slow steps toward the solution. In stochastic gradient descent, you’ll evaluate … Read more

The difficulty of searching through a solution space becomes much harder as you have more features (dimensions). Consider the analogy of looking for a penny in a line vs. a field vs. a building. The more dimensions you have, the higher volume of data you’ll need. The “Curse of Dimensionality” refers to the various challenges … Read more

Parametric models are those with a finite number of parameters. To predict new data, you only need to know the parameters of the model. Examples include linear regression, logistic regression, and linear SVMs. Non-parametric models are those with an unbounded number of parameters, allowing for more flexibility. To predict new data, you need to know … Read more

With the recent announcement of more breakthroughs in quantum computing, the question of how this new format and way of thinking through hardware serves as a useful proxy to explain classical computing and machine learning, and some of the hardware nuances that might make some algorithms much easier to do on a quantum machine. Demonstrating … Read more

If you’ve worked with external data sources, it’s likely you’ll have a few favorite APIs that you’ve gone through. You can be thoughtful here about the kinds of experiments and pipelines you’ve run in the past, along with how you think about the APIs you’ve used before. When answering the question “What are some of … Read more