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‘Deep Learning’ Interview Questions

What is Deep Learning? Discuss the key characteristics, working and applications of Deep Learning

What are the advantages and disadvantages of Deep Learning?

How does Deep Learning methods compare with traditional Machine Learning methods?

What is a Perceptron?

What is a Multilayer Perceptron (MLP)?

Explain the basic architecture and training process of a Neural Network model? Discuss briefly the key hyper-parameters

What is the difference between Deep and Shallow networks?

What is an activation function, and what are some of the most common choices for activation functions?

What is Backpropagation?

What are some options for making Backpropagation more efficient?

What do you mean by pretraining, finetuning and transfer learning?

What is the difference between a Batch and an Epoch?

Briefly describe the architecture of a Recurrent Neural Network (RNN) and how it addresses the shortcomings of traditional Neural Networks.

What is Long-Short Term Memory (LSTM)?

What are transformers? Discuss the evolution, advantages and major breakthroughs in transformer models

Explain the Transformer Architecture

What are the primary advantages of transformer models?

What are the limitations of transformer models?

Explain Self-Attention, and Masked Self-Attention as used in Transformers

What is Multi-head Attention and how does it improve model performance over single Attention head?

Explain the need for Positional Encoding in Transformer models

What is the “dead ReLU” problem and, why is it an issue in Neural Network training?

Explain Cross-Attention and how is it different from Self-Attention?

Why is Zero-centered output preferred for an activation function?

What do you mean by vanishing gradient and why is that a problem?

What do you mean by saturation in neural network training? Discuss the problems associated with saturation

What is an activation function? What are the different types of activation functions? Discuss their pros and cons

What are the key hyper-parameters of a neural network model?

Describe briefly the training process of a Neural Network model

What is Sigmoid (logistic) activation function?

Discuss TanH activation function

What is Rectified Linear Unit (ReLU) activation function? Discuss its advantages and disadvantages

Discuss Softmax activation function

What are some guidelines for choosing activation functions?

How are Regression and Classification performed using multilayer perceptrons (MLP)?

What is the vanishing/exploding gradient problem?

What are some ways to address the vanishing/exploding gradient issue?

What are some options to address overfitting in Neural Networks?

What is Dropout?

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