Deep Learning Interview Questions and Answers
Question - 31 : - What is exploding gradient descent in Deep Learning?
Answer - 31 : -
Exploding gradients are an issue causing a scenario that clumps up the gradients. This creates a large number of updates of the weights in the model when training.
The working of gradient descent is based on the condition that the updates are small and controlled. Controlling the updates will directly affect the efficiency of the model.
Question - 32 : - What is the use of LSTM?
Answer - 32 : -
LSTM stands for long short-term memory. It is a type of RNN that is used to sequence a string of data. It consists of feedback chains that give it the ability to perform like a general-purpose computational entity.
Question - 33 : - Where are autoencoders used?
Answer - 33 : -
Autoencoders have a wide variety of usage in the real world. The following are some of the popular ones:
- Adding color to black–white images
- Removing noise from images
- Dimensionality reduction
- Feature removal and variation
Question - 34 : - What are the types of autoencoders?
Answer - 34 : -
There are four main types of autoencoders:
- Deep autoencoders
- Convolutional autoencoders
- Sparse autoencoders
- Contractive autoencoders
Question - 35 : - What are some of the examples of supervised learning algorithms in Deep Learning?
Answer - 35 : -
There are three main supervised learning algorithms in Deep Learning:
- Artificial neural networks
- Convolutional neural networks
- Recurrent neural networks
Question - 36 : - Why is the Leaky ReLU function used in Deep Learning?
Answer - 36 : -
Leaky ReLU, also called LReL, is used to manage a function to allow the passing of small-sized negative values if the input value to the network is less than zero.
Question - 37 : - What are deep autoencoders?
Answer - 37 : -
Deep autoencoders are an extension of the regular autoencoders. Here, the first layer is responsible for the first-order function execution of the input. The second layer will take care of the second-order functions, and it goes on.
Usually, a deep autoencoder is a combination of two or more symmetrical deep-belief networks where:
- The first five shallow layers consist of the encoding part
- The other layers take care of the decoding part
On the next set of Deep Learning questions, let us look further into the topic.
Question - 38 : - Why is mini-batch gradient descent so popular?
Answer - 38 : -
Mini-batch gradient descent is popular as:
- It is more efficient when compared to stochastic gradient descent.
- Generalization is done by finding the flat minima.
- It helps avoid the local minima by allowing the approximation of the gradient for the entire dataset.
Question - 39 : - What are the variants of gradient descent?
Answer - 39 : -
There are three variants of gradient descent as shown below:
- Stochastic gradient descent: A single training example is used for the calculation of gradient and for updating parameters.
- Batch gradient descent: Gradient is calculated for the entire dataset, and parameters are updated at every iteration.
- Mini-batch gradient descent: Samples are broken down into smaller-sized batches and then worked on as in the case of stochastic gradient descent.
Question - 40 : - What are some of the limitations of Deep Learning?
Answer - 40 : -
There are a few disadvantages of Deep Learning as mentioned below:
- Networks in Deep Learning require a huge amount of data to train well.
- Deep Learning concepts can be complex to implement sometimes.
- Achieving a high amount of model efficiency is difficult in many cases.
These are some of the vital advanced deep learning interview questions that you have to know about!