ML Interview Questions

Explain how to use pretrained models like ResNet or VGG for new computer vision tasks.

Discuss the key differences, including techniques and challenges, between 2D and 3D computer vision tasks. How do these differences impact the choice of algorithms and the complexity of real-world applications?

Explain different approaches to object detection including R-CNN, YOLO, and SSD.

Explain different data augmentation techniques and their benefits.

Describe applications of GANs in computer vision including image generation and style transfer.

Describe the evolution of CNN architectures for image classification from AlexNet to modern models. What key innovations improved their performance over time?

Discuss modern approaches to implementing Optical Character Recognition (OCR) using deep learning models. How do these models address challenges such as varying fonts, languages, and image distortions?

Explain the differences between semantic, instance, and panoptic segmentation in computer vision. What are the challenges and recent advancements in each of these approaches?

Describe the pipeline of a facial recognition system, focusing on the stages from detection to identification, including any preprocessing steps, feature extraction methods, and classification techniques used.

Explain how you would handle class imbalance when working with image classification datasets. What are some techniques you can employ, and what are the potential benefits and drawbacks of each method?