Deep Learning Interview Questions and Answers
Question - 11 : - What are some of the Deep Learning frameworks or tools that you have used?
Answer - 11 : -
This question is quite common in a Deep Learning interview. Make sure to answer based on the experience you have with the tools.
However, some of the top Deep Learning frameworks out there today are:
- TensorFlow
- Keras
- PyTorch
- Caffe2
- CNTK
- MXNet
- Theano
Question - 12 : - What is the meaning of dropout in Deep Learning?
Answer - 12 : -
Dropout is a technique that is used to avoid overfitting a model in Deep Learning. If the dropout value is too low, then it will have minimal effect on learning. If it is too high, then the model can under-learn, thereby, causing lower efficiency.
Question - 13 : - How can hyperparameters be trained in neural networks?
Answer - 13 : -
Hyperparameters can be trained using four components as shown below:
- Batch size: This is used to denote the size of the input chunk. Batch sizes can be varied and cut into sub-batches based on the requirement.
- Epochs: An epoch denotes the number of times the training data is visible to the neural network so that it can train. Since the process is iterative, the number of epochs will vary based on the data.
- Momentum: Momentum is used to understand the next consecutive steps that occur with the current data being executed at hand. It is used to avoid oscillations when training.
- Learning rate: Learning rate is used as a parameter to denote the time required for the network to update the parameters and learn.
Next up on this top Deep Learning interview questions and answers blog, let us take a look at the intermediate questions.
Question - 14 : - What are hyperparameters in Deep Learning?
Answer - 14 : -
Hyperparameters are variables used to determine the structure of a neural network. They are also used to understand parameters, such as the learning rate and the number of hidden layers, and more, present in the neural network.
Question - 15 : - What is backpropagation?
Answer - 15 : -
Backpropagation is used to minimize the cost function by first seeing how the value changes when weights and biases are tweaked in the neural network. This change is easily calculated by understanding the gradient at every hidden layer. It is called backpropagation as the process begins from the output layer, moving backward to the input layers.
Question - 16 : - What is forward propagation?
Answer - 16 : -
Forward propagation is the scenario where inputs are passed to the hidden layer with weights. In every single hidden layer, the output of the activation function is calculated until the next layer can be processed. It is called forward propagation as the process begins from the input layer and moves toward the final output layer.
Question - 17 : - What is data normalization in Deep Learning?
Answer - 17 : -
Data normalization is a preprocessing step that is used to refit the data into a specific range. This ensures that the network can learn effectively as it has better convergence when performing backpropagation.
Question - 18 : - Differentiate between a single-layer perceptron and a multi-layer perceptron.
Answer - 18 : -
| Single-layer Perceptron | Multi-layer Perceptron |
| Cannot classify non-linear data points | Can classify non-linear data |
| Takes in a limited amount of parameters | Withstands a lot of parameters |
| Less efficient with large data | Highly efficient with large datasets |
Question - 19 : - What are the steps to be followed to use the gradient descent algorithm?
Answer - 19 : -
There are five main steps that are used to initialize and use the gradient descent algorithm:
- Initialize biases and weights for the network
- Send input data through the network (the input layer)
- Calculate the difference (the error) between expected and predicted values
- Change values in neurons to minimize the loss function
- Multiple iterations to determine the best weights for efficient working
Question - 20 : - What are autoencoders?
Answer - 20 : -
Autoencoders are artificial neural networks that learn without any supervision. Here, these networks have the ability to automatically learn by mapping the inputs to the corresponding outputs.
Autoencoders, as the name suggests, consist of two entities:
- Encoder: Used to fit the input into an internal computation state
- Decoder: Used to convert the computational state back into the output