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Thursday, 27 June 2019

100+ Basic Deep Learning Interview Questions and Answers

I have listed down some basic deep learning interview questions with answers. These deep learning interview questions cover many concepts like perceptrons, neural networks, weights and biases, activation functions, gradient descent algorithm, CNN (ConvNets), CapsNets, RNN, LSTM, regularization techniques, dropout, hyperparameters, transfer learning, fine-tuning a model, autoencoders, NLP (Natural Language Processing), deep learning frameworks like TensorFlow and Keras, Google Cloud Vision API etc. I will keep adding more and more deep learning interview questions in this list. So, stay tuned.

Note: For Machine Learning Interview Questions, refer this link.

Introduction

1. What is Deep Learning? How is it different from machine learning? What are the pros and cons of deep learning over machine learning? Answer

2. How does deep learning mimic the behavior of human brain? How will you compare an artificial neuron to a biological neuron?

Perceptron

3. What is a Perceptron? How does it work? What is a multi-layer perceptron?

4. What are the various limitations of a Perceptron? Why cannot we implement XOR gate using Perceptron?

Answers to above questions

Neural Networks

5. What are the various layers in a neural network?

6. What are the various types of a neural network?

7. What are Deep and Shallow neural networks? What are the advantages and disadvantages of deep neural networks over shallow neural networks?

Answers to above questions

Weights and Bias

8. What is the importance of weights and biases in a neural network? What are the things to keep in mind while initializing weights and biases? Answer

9. What is Xavier Weight Initialization technique? How is it helpful in initializing the weights? How does weight initialization vary for different types of activation functions? Answer 

10. Explain forward and backward propagation in a neural network. How does a neural network update weights and biases during back propagation? (See Gradient Descent section for answer)

Activation Functions

11. What do you mean by activation functions in neural networks? Why do we call them squashing functions? How do activation functions bring non-linearity in neural networks?

12. Explain various activation functions like Step (Threshold)Logistic (Sigmoid), Hyperbolic Tangent (Tanh), and ReLU (Rectified Linear Unit)What are the various advantages and disadvantages of using these activation functions? 

Answers to above questions

13. Dying and Leaky ReLU: What do you mean by Dying ReLU? When a neuron is considered as dead in a neural network? How does leaky ReLU help in dealing with dying ReLU? Answer

14. What is the difference between Sigmoid and Softmax activation functions? Answer

Batches

15. Explain the terms: EpochsBatches and Iterations in neural networks.

16. What do you mean by Batch Normalization? What are its various advantages? Answer

Loss Function

17. What is the difference between categorical_crossentropy and sparse_categorical_crossentropy? Which one to use and when?

Hint: For one hot encoded labels, use categorical_crossentropy. Otherwise, use sparse_categorical_crossentropy.

Gradient Descent

18. What is Gradient Descent? How is it helpful in minimizing the loss function? What are its various types? 

19. Explain Batch, Stochastic, and Mini Batch Gradient Descent. What are the advantages and disadvantages of these Gradient Descent methods? Answer

20. Explain these terms in context of SGD: Momentum, Nesterov Momentum, AdaGrad, AdaDelta, RMSprop, Adam. Answer

21. What is the difference between Local and Global Minima? What are the ways to avoid local minima? Answer

22. Explain Vanishing and Exploding Gradients.

23. What is Learning Rate? How does low and high learning rate affect the performance and accuracy of a neural network? Answer

24. If loss in a neural network is not decreasing during training period after so many iterations, what could be the possible reasons?

Hint: Think of low / high learning rate, local and global minima (may be it stuck at local minima), high regularization parameter etc.

CNN (ConvNets)

25. What is Convolutional Neural Network? Explain various layers in a CNN? 

26. What are the Filters (Kernels) in CNN? What is Stride?

27. What do you mean by Padding in CNN? What is the difference between Zero Padding and Valid Padding?

28. What do you mean by Pooling in CNN? What are the various types of pooling? Explain Max Pooling, Min Pooling, Average Pooling and Sum Pooling.

29. What are the various hyperparameters in CNN which need to be tuned while training process?

30. How is CNN different from traditional fully connected neural networks? Why we cannot use fully connected neural networks for image recognition?

31. Suppose we have an input of n X n dimension and filter of f X f dimension. If we slide this filter over the input in the convolutional layer, what will be the dimension of the resulting output?

Answers to above questions

CapsNets

32. What is Capsule Neural Network (CapsNets)? How is it different from CNN (ConvNets)? Answer

Computer Vision

33. What is computer vision? How does deep learning help in solving various computer vision problems? Answer

RNN

34. Explain RNN (Recurrent Neural Network). Why is RNN best suited for sequential data?

35. What do you mean by feedback loop in RNN?

36. What are the various types of RNN? Explain with example: One to One, One to Many, Many to One, and Many to Many RNN.

37. What is Bidirectional RNN?

38. What are the various issues with RNN? Explain Vanishing and Exploding Gradients. What are the various ways to solve these gradient issues in RNN?

39. What are the various advantages and disadvantages of RNN?

40. What are the various applications of RNN?

41. What are the differences between CNN and RNN?

LSTM

42. How does LSTM (Long Short Term Memory) solve Vanishing Gradient issue in RNN?

43. What are the gated cells in LSTM? What are the various types of gates used in LSTM?

44. What are the various applications of LSTM?

Answers to all questions of RNN and LSTM

Regularization

45. What are the main causes of overfitting and underfitting in a neural network?

46. What are the various regularization techniques used in a neural network?

47. Explain L1 and L2 Regularization techniques used in a neural network.

48. What is Dropout? How does it prevent overfitting in a neural network? What are its various advantages and disadvantages? Answer

49. What is Data AugmentationHow does it prevent overfitting in a neural network?

50. What is Early Stopping? How does it prevent overfitting in a neural network?

Answers to above questions

Learnable Parameters and Hyperparameters

51. What are the learnable parameters in a neural network? Explain with an example.

52. What are the various hyperparameters used in a neural network? What are the various ways to optimize these hyper-parameters?

Answers to above questions

53. How will you manually calculate number of weights and biases in a fully connected neural network? Explain with an example. YouTube video

54. How will you manually calculate number of weights and biases in a convolutional neural network (CNN)? Explain with an example. YouTube video

Transfer Learning

55. What do you mean by Transfer Learning and Fine-tuning a model? What are its various advantages? What are the various steps to fine-tune a model? Answer

Autoencoders

56. What are Autoencoders? What are the various components of an autoencoder? Explain encoder, decoder and bottleneckHow does an autoencoder work?

57. What do you mean by latent space representation and reconstruction loss in an autoencoder?

58. What are the various properties of an autoencoder?

59. What are the various types of an autoencoder? Explain Undercomplete autoencoder, Sparse autoencoder, Denoising autoencoder, Convolutional autoencoder, Contractive autoencoders and Deep autoencoders.

60. How do we add regularization capabilities to autoencoders?

61. What are the various applications of an autoencoder?

62. What are the various hyperparameters we need to tune in an autoencoder?

63. How will you compare Autoencoders with PCA (Principal Component Analysis)?

64. What is RBM (Restricted Boltzman Machine)? What is the difference between an Autoencoder and RBM?

Answers to above questions

NLP (Natural Language Processing)

65. What are the various steps involved in preprocessing of the raw data in NLP? Explain Tokenization, Stemming, Lemmatization and Vectorization.

66. What is the difference between Stemming and Lemmatization?

67. What are the various types of Vectorization? Explain Count Vectorization, N-grams Vectorization and Term Frequency - Inverse Document Frequency (TF-IDF).

Answers to above questions

Frameworks

68. What are the various frameworks available to implement deep learning models? What should be the characteristics of an ideal deep learning framework? Answer

TensorFlow

69. Explain TensorFlow architecture.

70. What is a Tensor? Explain Tensor Datatypes and Ranks.

71. What are Constants, Placeholders and Variables in a TensorFlow? Why do we need to initialize variables explicitly?

72. What is a Computational Graph? What are the nodes and edges in it? How to build and run the graph using session? What are its various advantages?

73. What is a Tensor Board? How is it useful?

74. What is a TensorFlow Pipeline? How is it useful?

75. Explain these terms: Feed Dictionary and Estimators

Answers to above questions

76. Write a sample code to demonstrate constants, placeholders and variables in TensorFlow? Answer

77. Write a sample code using TensorFlow to demonstrate gradient descent? Answer

78. Implement a Linear Classification Model using TensorFlow Estimator. Answer

Keras

79. What do you know about Keras framework? What are its various advantages and limitations? Answer

80. How will you build a basic sequential model using Keras? Answer 

81. How will you solve a regression problem using sequential model in Keras? Answer

82. How will you build a basic CNN model using Keras? Answer 

83. How will you build a basic LSTM model using Keras?

84. What are the various pre-trained models available in Keras? How are these pre-trained models useful for us?

85. How will you use VGG16 model to recognize a given image? Answer

86. How will you fine-tune VGG16 model for image classification? Answer

87. How will you fine-tune MobileNet model for image classification? What is the difference between VGG16 and MobileNet model?

Google Cloud Vision API

88. How will you use Google Cloud Vision API for Label Detection? Answer

89. How will you use Google Cloud Vision API for Text Detection (OCR: Optical Character Recognition)? Answer

Some of the above questions don't have answers by now. I am still writing answers for these questions and will keep this list updated. Although above list does not contain 100+ questions as claimed in the title of the post, but very soon I will take the count beyond 100.

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I am currently messing up with neural networks in deep learning. I am learning Python, TensorFlow and Keras.

Author: I am an author of a book on deep learning.

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