ML Interview Questions

Explain the concept of overfitting in neural networks and discuss at least three different regularization techniques that can be used to mitigate it. Illustrate how each technique affects the model both theoretically and practically, possibly including examples or diagrams.

Describe the architecture of a Generative Adversarial Network (GAN) and explain the training process. What are some of the common challenges faced when training GANs?

Explain the core components and operations of Convolutional Neural Networks (CNNs) and discuss why CNNs are particularly effective for image processing and computer vision tasks, as compared to traditional fully-connected neural networks.

Explain the architecture and functioning of Generative Adversarial Networks (GANs). Discuss their key components, typical challenges encountered during training, and highlight some recent advancements in GAN technology.

Describe the Transformer architecture in detail, focusing on its key components such as the attention mechanism and positional encoding. Discuss how these components contribute to its success in natural language processing (NLP) tasks and compare it to traditional RNN-based models. How can Transformers be adapted for tasks beyond NLP, such as image processing or time series forecasting?

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?