Deep Learning Interview Questions and Answers
Question - 21 : - What is the use of the swish function?
Answer - 21 : -
The swish function is a self-gated activation function developed by Google. It is now a popular activation function used by many as Google claims that it outperforms all of the other activation functions in terms of computational efficiency.
Question - 22 : - What are tensors?
Answer - 22 : -
Tensors are multidimensional arrays in Deep Learning that are used to represent data. They represent the data with higher dimensions. Due to the high-level nature of the programming languages, the syntax of tensors is easily understood and broadly used.
Question - 23 : - What is the meaning of model capacity in Deep Learning?
Answer - 23 : -
In Deep Learning, model capacity refers to the capacity of the model to take in a variety of mapping functions. Higher model capacity means a large amount of information can be stored in the network.
We will check out neural network interview questions alongside as it is also a vital part of Deep Learning.
Question - 24 : - What is a Boltzmann machine?
Answer - 24 : -
A Boltzmann machine is a type of recurrent neural network that uses binary decisions, alongside biases, to function. These neural networks can be hooked up together to create deep belief networks, which are very sophisticated and used to solve the most complex problems out there.
Question - 25 : - What are some of the advantages of using TensorFlow?
Answer - 25 : -
TensorFlow has numerous advantages, and some of them are as follows:
- High amount of flexibility and platform independence
- Trains using CPU and GPU
- Supports auto differentiation and its features
- Handles threads and asynchronous computation easily
- Open-source
- Has a large community
Question - 26 : - What is a computational graph in Deep Learning?
Answer - 26 : -
A computation graph is a series of operations that are performed to take inputs and arrange them as nodes in a graph structure. It can be considered as a way of implementing mathematical calculations into a graph. This helps in parallel processing and provides high performance in terms of computational capability.
Question - 27 : - What is a CNN?
Answer - 27 : -
CNNs are convolutional neural networks that are used to perform analysis on images and visuals. These classes of neural networks can input a multi-channel image and work on it easily.
These Deep Learning questions must be answered in a concise way. So make sure to understand them and revisit them if necessary.
Question - 28 : - What are the various layers present in a CNN?
Answer - 28 : -
There are four main layers that form a convolutional neural network:
- Convolution: These are layers consisting of entities called filters that are used as parameters to train the network.
- ReLu: It is used as the activation function and is always used with the convolution layer.
- Pooling: Pooling is the concept of shrinking the complex data entities that form after convolution and is primarily used to maintain the size of an image after shrinkage.
- Connectedness: This is used to ensure that all of the layers in the neural network are fully connected and activation can be computed using the bias easily.
Question - 29 : - What is an RNN in Deep Learning?
Answer - 29 : -
RNNs stand for recurrent neural networks, which form to be a popular type of artificial neural network. They are used to process sequences of data, text, genomes, handwriting, and more. RNNs make use of backpropagation for the training requirements.
Question - 30 : - What is a vanishing gradient when using RNNs?
Answer - 30 : -
Vanishing gradient is a scenario that occurs when we use RNNs. Since RNNs make use of backpropagation, gradients at every step of the way will tend to get smaller as the network traverses through backward iterations. This equates to the model learning very slowly, thereby, causing efficiency problems in the network.