Machine Learning Engineer Concept Interview Questions

Vishwanath K. - "DevOps Engineer Interview Questions for 3+ yrs experience candidate"See full answer

William M. - "Relu = 0 if > some threshold else x sigmoid normalizes to 0-1 asymptotically"See full answer

Alan T. - "The difference between convex and nonconvex functions lies in their mathematical properties and the implications for optimization problems. Convex Functions:A convex function has a shape where any line segment connecting two points on its graph lies entirely above or on the graph. This property ensures that any local minimum is also a global minimum, making optimization straightforward and reliable. Convex functions are critical in machine learning and optimization tasks because of th"See full answer

Woosung J. - "For a dataset with one million data points, a Deep Neural Network (DNN) is almost always the superior choice over a K-Nearest Neighbors (KNN) algorithm. The primary reasons for this preference involve computational efficiency, scalability, and the ability to handle high-dimensional data. 1. Computational Complexity (Inference Time) KNN: It is a "lazy learner." It doesn't actually "learn" a model; instead, it stores the entire dataset. To make a single prediction, it must calcul"See full answer

Satyam C. - "TF-IDF CONCEPT EXPLANATION AND INTUITION BUILDING: TF-IDF is a measure that reflects the importance of a word in the document relative to a collection of documents. Its full form is Term Frequency - Inverse Document Frequency. The term TF indicates how often a term occurs in a particular document. It is the ratio of count of a particular term in a document to the number of terms in that particular document. So, the intuition is that if a term occurs frequently in a single documen"See full answer

Shibin P. - "Effective loss functions for computer vision models vary depending on the specific task, some commonly used loss functions for different tasks: Classification Cross-Entropy Loss:Used for multi-class classification tasks. Measures the difference between the predicted probability distribution and the true distribution. Binary Cross-Entropy Loss:Used for binary classification tasks. Evaluates the performance of a model by comparing predicted probabilities to the true binary labe"See full answer

Lash - "A perceptron is the most basic building block of a neural network and represents a single-layer binary classifier."See full answer

Matt M. - "Given the dataset does not contain many labels, it implies we cannot directly use supervised learning. I would ask more about the type of dataset we are given. Is it images, text, etc? This may inform the types of transformations we do the dataset. I can see two approaches to training Given the labels we do have, we can find a method to generate labels for the other unlabeled data. This likely will introduce some error since they may not be true labels, but it at least allows processing the"See full answer

Bhavya V. - "Overfitting is the condition where your model is giving an unexpectedly higher accuracy because of its training in a small database and not getting exposed to anu different type of database while testing"See full answer