ML Interview Questions

Explain transfer learning and when it should be used in deep learning projects.

Describe the transformer architecture in detail and explain the role of key components such as self-attention, positional encoding, and the feed-forward neural network in the context of natural language processing (NLP) tasks.

Explain the backpropagation algorithm and how it is used to optimize neural networks in the context of gradient descent. How does it relate to the chain rule, and what are some potential pitfalls or challenges that can arise during its implementation?

Explain the differences between Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs) in terms of their architecture, capabilities, and typical applications. What are the advantages and limitations of each? Provide a simple code example to illustrate the implementation of each architecture using either TensorFlow or PyTorch.

Explain the vanishing and exploding gradient problems in deep neural networks and discuss various strategies to mitigate these issues. Provide examples of how these problems manifest in practice.

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?