ML Interview Questions

Can you explain the vanishing gradient problem in deep neural networks and discuss several methods to mitigate it?

Explain batch normalization in deep learning. How does it work, and what are its benefits and limitations?

Describe and compare the ReLU, sigmoid, tanh, and other common activation functions used in neural networks. Discuss their characteristics, advantages, and limitations, and explain in which scenarios each would be most suitable.

Explain the key components of a Convolutional Neural Network (CNN) architecture, detailing the purpose of each component. How have CNN architectures evolved over time to improve performance and efficiency? Provide examples of notable architectures and their contributions.

Describe how backpropagation is utilized to optimize neural networks. What are the mathematical foundations of this process, and how does it impact the learning of the model?

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.