100 Interview Questions for Deep Learning

If you’re looking for a job in deep learning, you’ll need to be prepared to answer some tough questions. In order to help you get started, we’ve put together a list of 100 interview questions for deep learning. While many of these questions are related to deep learning concepts, we have also listed several frameworks (Tensorflow, Pytorch, etc) related questions. By being prepared for these questions, you’ll be able to demonstrate your knowledge and expertise in this area, and increase your chances of landing the job!

1. What is deep learning?
2. How does machine learning differ from deep learning?
3. What are the differences between shallow and deep learning?
4. How does deep learning work?
5. What are some common deep learning architectures?
6. How do you choose the right deep learning architecture for your problem?
7. What are the benefits of using deep learning?
8. Are there any limitations to using deep learning?
9. Why is interpretability important for deep learning models and how do you measure interpretability of deep learning models predictions?
10. How can businesses use deep learning?
11. What challenges do businesses face when implementing deep learning?
12. What are some recent breakthroughs in deep learning?
13. Who pioneered the development of deep learning algorithms and techniques?
14. What are some real-world use cases of deep learning?
15. What are the future applications of deep learning?
16. Can you provide some examples of companies or organizations using deep learning?
17. What is a neural network?
18. How does a neural network work?
19. What is Perceptron? What is multi-layer perceptron (MLP)?
20. What is a deep neural network?
21. How do you train a deep neural network?
22. What is backpropagation, and how does it work?
23. What are some applications of neural networks?
24. What are the different types of architectures for deep neural networks?
25. How do you choose the right size for your deep neural network?
26. What are the best practices for debugging deep neural networks?
27. How can you prevent overfitting in your deep learning models?
28. What is recurrent neural network (RNN)?
29. How does a recurrent neural network work?
30. What are the different types of RNNs?
31. How do you choose the right RNN for your task?
32. Why are recurrent neural networks useful?
33. How do you design a recurrent neural network?
34. What are the best practices for training recurrent neural networks?
35. How do you debug a recurrent neural network?
36. What are the limitations of recurrent neural networks?
37. What is a convolutional neural network (CNN)? Why use CNN?
38. How does a convolutional neural network work?
39. When to use convolutional or fully connected layers?
40. Why are convolutional neural networks popular for image recognition?
41. What type of data is best suited for a convolutional neural network?
42. How many layers should a Convolutional Neural Network have?
43. Is it better to use mini batches or full batches when training a Convolutional Neural Network?
44. When should you use dropout in a Convolutional Neural Network?
45. What are the benefits of using ReLU activations in a Convolutional Neural Network?
46. What is an activation function?
47. What are the different types of activation functions?
48. How do you choose the right activation function for your neural network?
49. How do you determine how much to adjust your input data when using an activation function?
50. What impact does an activation function have on learning speed and performance in a neural network?
51. When is it appropriate to use a linear activation function instead of another type of activation function?
52. What is Tensorflow?
53. What are the benefits of using Tensorflow?
54. How is Tensorflow different from other deep learning frameworks?
55. What are some best practices for working with TensorFlow?
56. What are some common pitfalls when using TensorFlow?
57. How can I debug errors in my TensorFlow code?
58. What are some ways to improve performance of my TensorFlow code?
59. How do you implement a CNN in Tensorflow?
60. How do you implement a RNN in Tensorflow?
61. What is PyTorch?
62. How does Pytorch compare to TensorFlow?
63. How do you implement a CNN in PyTorch?
64. How do you implement a RNN in PyTorch?
65. What is a transformer in deep learning?
66. How does a transformer work?
67. What are some advantages & disadvantages of using transformers in deep learning?
68. What is the difference between a regular transformer and a stacked transformer?
69. How can you optimize a transformer for your specific needs?
70. What is an autoencoder?
71. What are the differences between a supervised and unsupervised autoencoder?
72. How do you train an autoencoder?
73. What are the benefits of using an autoencoder?
74. What is an LSTM?
75. How does an LSTM work?
76. What are the benefits of using an LSTM?
77. What are some applications of LSTMs?
78. How can you train and configure an LSTM for your needs?
79. Are there any issues with using LSTMs?
80. How do you debug and troubleshoot issues with an LSTM?
81. – What are some best practices for working with LSTMs?
82. What are the benefits of using deep learning for image recognition?
83. How does deep learning work for image recognition?
84. How do you choose the right type of deep learning architecture for image recognition?
85. What are pre-processing techniques for images?
86. How do you perform feature extraction in images?
87. What is a convolutional neural network and how is it used in image recognition?
88. How do you train a convolutional neural network for image recognition?
89. How does deep learning work for audio or speech recognition?
90. What are the benefits of using deep learning for audio or speech recognition?
91. How is a neural network trained for audio or speech recognition?
92. How does a convolutional neural network differ from a traditional neural network for audio or speech recognition?
93. What are the limitations of deep learning for audio or speech recognition?
94. How does deep learning help in video recognition?
95. What are the limitations of deep learning for video recognition?
96. What is a good way to initialize a deep learning network for video recognition?
97. How do you choose the right number of layers and neurons for a deep learning network for video recognition?
98. What are some common problems with training deep learning networks for video recognition?
99. How do you detect objects in videos using deep learning?
100. What are some ways to improve the accuracy of deep learning networks for video recognition?