Deep Learning Interview Questions and Answers
Question - 41 : - What is a Restricted Boltzmann Machine?
Answer - 41 : -
A Restricted Boltzmann Machine, or RBM for short, is an undirected graphical model that is popularly used in Deep Learning today. It is an algorithm that is used to perform:
- Dimensionality reduction
- Regression
- Classification
- Collaborative filtering
- Topic modeling
Next up on this top Deep Learning interview questions and answers blog, let us take a look at the advanced questions.
Question - 42 : - What are some of the examples of unsupervised learning algorithms in Deep Learning?
Answer - 42 : -
There are three main unsupervised learning algorithms in Deep Learning:
- Autoencoders
- Boltzmann machines
- Self-organizing maps
Next up, let us look at more neural network interview questions that will help you ace the interviews.
Question - 43 : - Can we initialize the weights of a network to start from zero?
Answer - 43 : -
Yes, it is possible to begin with zero initialization. However, it is not recommended to use because setting up the weights to zero initially will cause all of the neurons to produce the same output and the same gradients when performing backpropagation. This means that the network will not have the ability to learn at all due to the absence of asymmetry between each of the neurons.
Question - 44 : - What is the meaning of valid padding and same padding in CNN?
Answer - 44 : -
- Valid padding: It is used when there is no requirement for padding. The output matrix will have the dimensions (n – f + 1) X (n – f + 1) after convolution.
- Same padding: Here, padding elements are added all around the output matrix. It will have the same dimensions as the input matrix.
Question - 45 : - What are some of the applications of transfer learning in Deep Learning?
Answer - 45 : -
Transfer learning is a scenario where a large model is trained on a dataset with a large amount of data and this model is used on simpler datasets, thereby resulting in extremely efficient and accurate neural networks.
The popular examples of transfer learning are in the case of:
Question - 46 : - How is the transformer architecture better than RNNs in Deep Learning?
Answer - 46 : -
With the use of sequential processing, programmers were up against:
- The usage of high processing power
- The difficulty of parallel execution
This caused the rise of the transformer architecture. Here, there is a mechanism called attention mechanism, which is used to map all of the dependencies between sentences, thereby making huge progress in the case of NLP models.
Question - 47 : - What are the steps involved in the working of an LSTM network?
Answer - 47 : -
There are three main steps involved in the working of an LSTM network:
- The network picks up the information that it has to remember and identifies what to forget.
- Cell state values are updated based on Step 1.
- The network calculates and analyzes which part of the current state should make it to the output.
Question - 48 : - What are the elements in TensorFlow that are programmable?
Answer - 48 : -
In TensorFlow, users can program three elements:
- Constants
- Variables
- Placeholders
Question - 49 : - What is the meaning of bagging and boosting in Deep Learning?
Answer - 49 : -
Bagging is the concept of splitting a dataset and randomly placing it into bags for training the model.
Boosting is the scenario where incorrect data points are used to force the model to produce the wrong output. This is used to retrain the model and increase accuracy.
Question - 50 : - What are generative adversarial networks (GANs)?
Answer - 50 : -
Generative adversarial networks are used to achieve generative modeling in Deep Learning. It is an unsupervised task that involves the discovery of patterns in the input data to generate the output.
The generator is used to generate new examples, while the discriminator is used to classify the examples generated by the generator.