Top 100 Deep Learning Interview Questions for Freshers and Experienced

Deep Learning Interview Questions for Freshers: Artificial Intelligence is no longer a buzzword, it’s a booming career field and everyone knows about – from a tiny child to your neighbour granny – it’s everywhere!

If you’re eyeing a position in machine learning or AI, you’ll need to face some tough yet interesting deep learning interview questions. Whether you’re a fresher trying to break into the industry or an experienced engineer aiming to climb the ladder, this blog has you covered.

In this blog, Ze Learning Labb will give you some deep learning interview questions that recruiters often ask, from theory-based ones to coding tasks and domain-specific sections like NLP and computer vision. As an added bonus, we’ll also sprinkle in tips, real quotes from hiring experts, and resources to help you gear up for success – because ZELL really wants to see you thrive professionally!

Why Interviewers Ask Deep Learning Questions?

Hiring managers want to gauge your understanding, problem-solving ability, and hands-on skills. The domain’s evolving fast, so they want candidates who can think in layers, quite literally!

1. Deep Learning Interview Questions for Freshers

Getting your basics right is non-negotiable. Freshers should focus on foundational topics such as neural networks, activation functions, backpropagation, and optimizers.

Here are some questions interviewers love to ask:

Basic theory:

1. What is deep learning, and how is it different from machine learning?
2. Explain the architecture of a simple neural network.
3. What are activation functions? Name a few commonly used ones.
4. How does ReLU function differ from Sigmoid?
5. What is backpropagation? How does it work?

Application-based:

6. What’s the difference between underfitting and overfitting?
7. What techniques can you use to prevent overfitting?
8. What is dropout in deep learning?
9. What is the vanishing gradient problem?
10. How does batch normalization help?

Use case question:

“If you’re building a handwritten digit recogniser for postal letters, which kind of deep learning model would you use and why?”

2. Deep Learning Interview Questions for Experienced

If you’ve been in the industry for 2-5+ years, you’re expected to know the intricacies of neural architectures, production environments, and model evaluation techniques.

Architecture & Training:

11. How does ResNet solve the problem of vanishing gradients?
12. What’s the difference between LSTM and GRU?
13. Explain attention mechanism in deep learning.
14. What are GANs? How do they work?
15. What is transfer learning?

Evaluation:

16. How do you select the right loss function for your task?
17. What’s precision vs recall vs F1-score?
18. What is the ROC curve and how is it useful?
19. How do you choose hyperparameters in a neural network?
20. What are early stopping and learning rate schedules?

These deep learning interview questions for experienced candidates can help recruiters assess your ability to apply models in real-world settings.

3. Computer Vision Deep Learning Interview Questions

With facial recognition, medical imaging, and autonomous vehicles on the rise, computer vision is in huge demand. Here are computer vision deep learning interview questions to sharpen your prep:

21. What is convolution and how does it work in CNNs?
22. What is the role of pooling layers?
23. How does padding help in image classification?
24. What’s the difference between max pooling and average pooling?
25. What is image augmentation?
26. What is YOLO, and where is it used?
27. What’s the difference between classification and detection?
28. Explain semantic segmentation vs instance segmentation.
29. How does transfer learning apply in computer vision?
30. What is the role of OpenCV in deep learning?

4. Deep Learning NLP Interview Questions

With AI assistants, chatbots, and sentiment analysis tools everywhere, NLP is another domain buzzing with opportunities. Here are some deep learning NLP interview questions:

31. What is word embedding?
32. How does Word2Vec work?
33. What is the role of RNNs in NLP?
34. Explain how transformers work.
35. What is BERT and how does it differ from LSTM?
36. How does tokenization affect NLP models?
37. What are attention scores?
38. Explain masked language modelling.
39. How is BLEU score used in NLP?
40. What is sequence-to-sequence modelling?

These questions are vital for those targeting NLP-specific roles and are becoming more frequent in interview questions for deep learning.

5. Deep Learning Coding Interview Questions

Let’s be honest, coding questions can make or break the interview. Interviewers want to see if you can write clean, efficient, and functional code.

Python-based Deep Learning Coding Tasks:

41. Code a simple feedforward neural network using Keras.
42. Implement a CNN from scratch in TensorFlow.
43. Write a PyTorch model to classify MNIST digits.
44. How do you load and preprocess image datasets?
45. Implement a custom loss function.

Troubleshooting:

46. What would you do if your model accuracy plateaus at 60%?
47. How do you detect and fix data imbalance?
48. Write code to implement early stopping.
49. How do you tune hyperparameters using grid search?
50. How do you visualise model predictions using matplotlib?

These deep learning coding interview questions aren’t just about syntax, they test your thinking process too.

Top Interview Tips & Learning Resources by Ze Learning Labb

Tips to ace your deep learning interview:

• Revise basics daily, especially activation functions, CNNs, and backpropagation.
• Stay updated, follow top AI journals and GitHub repos.
• Mock interviews, practice with peers or mentors.
• Project portfolio, build your GitHub with real-world AI applications.

Enrol in Ze Learning Labb Courses:

If you’re serious about cracking interviews and building hands-on skills, check out Ze Learning Labb, one of India’s most practical and outcome-focused learning platforms:

• Data Science Course: Learn Python, ML, DL and get industry-ready.
• Data Analytics Course: Focused on tools like Excel, PowerBI, SQL, Tableau.
• Digital Marketing Course: Upskill for content, SEO, SEM and analytics.

These courses blend theory with real-life projects and offer mentorship that freshers and pros alike will find valuable.

Still Curious? Here’s 50 More Questions

We promised you 100, and here’s the rest:

51. What is the difference between AI, ML, and DL?
52. Define epoch and iteration in deep learning.
53. What is data normalization?
54. What is the function of an optimizer?
55. What is Adam optimizer?
56. What is Xavier initialization?
57. Define receptive field in CNN.
58. What is one-hot encoding?
59. Difference between batch and stochastic gradient descent?
60. What are autoencoders?
61. Define overfitting with an example.
62. What is the curse of dimensionality?
63. Define feature extraction.
64. What is the role of bias and weight in neural networks?
65. What are residual connections?
66. Explain softmax activation function.
67. How does dropout act as regularisation?
68. What is the exploding gradient problem?
69. Define multi-class vs multi-label classification.
70. What is a confusion matrix?
71. How do you measure model interpretability?
72. What is pruning in neural networks?
73. Difference between precision and accuracy.
74. What is multi-layer perceptron?
75. Explain kernel size in CNN.
76. What is a tensor in PyTorch?
77. How does TensorFlow differ from PyTorch?
78. Explain gradient clipping.
79. What is dilated convolution?
80. What is a Siamese network?
81. Define anchor boxes in object detection.
82. What is focal loss?
83. How do you detect overfitting during training?
84. Explain the use of callbacks in Keras.
85. Define triplet loss function.
86. What is self-attention?
87. Explain the transformer architecture in brief.
88. How does GPT model work?
89. What is language modeling?
90. What are contextual embeddings?
91. How to evaluate image segmentation performance?
92. What are the main differences between CNN and RNN?
93. What is beam search in NLP?
94. How does data augmentation help?
95. What is label smoothing?
96. Difference between validation loss and training loss.
97. What is a confusion matrix?
98. How do ensemble models work?
99. How do you freeze layers in a pretrained model?
100. Define greedy decoding in NLP.

On A Final Note…

Whether you’re preparing for deep learning interview questions for freshers or brushing up on your skills with deep learning interview questions for experienced folks, the key lies in practice, consistency, and hands-on learning.

By mastering concepts across NLP, computer vision, and model evaluation and tackling interview questions for deep learning like a pro, you’ll be steps ahead in the hiring game.

And remember, the journey doesn’t stop at interviews, keep building, keep learning. Ready to make your mark in AI? Check out Ze Learning Labb’s courses and start shaping your future today. Call us today!