## Deep Learning Interview Questions and Answers

**Deep Learning Interview Questions and Answers** for beginners and experts. List of frequently asked Deep Learning Questions with answers by Besant Technologies. We hope these Deep Learning interview questions and answers are useful and will help you to get the best job in the networking industry. These Deep Learning interview questions and answers are prepared by Deep Learning Professionals based on MNC Company’s expectations. Stay tuned we will update New Deep Learning Interview questions with Answers Frequently.

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### Best Deep Learning Interview Questions and Answers

Here is the list of most frequently asked **Deep Learning Interview Questions and Answers** in technical interviews. These Deep Learning questions and answers are suitable for both freshers and experienced professionals at any level. The Deep Learning questions are for intermediate to somewhat advanced Deep Learning professionals, but even if you are just a beginner or fresher you should be able to understand the Deep Learning answers and explanations here we give.

In this post, you will get the most important and top Deep Learning Interview Questions and Answers, which will be very helpful and useful to those who are preparing for jobs.

Q1) What about Deep Learning?

The world is going digital and deep learning has a huge role in this growth. Today, deep learning is been seen as one of the advanced pieces of technology. However, just like other aspects, deep learning has seen its own shares of challenges in the past. The original concept of deep learning revolves around the deep artificial neural network which was highly inspired by the brain networks. Speech recognition, self-driven cars, object classification, and data pattern are some of the most common areas where deep learning has played a huge role.

Q2) What is the difference between a deep network and shallow network?

The experts at the Besant Technologies explains that both deep and shallow learning can be utilized in the very same function. But as we talk about functionality, deep learning comes with more layers which helps it to perform better than shallow learning that has fewer parameters.

Q3) What is the cost function in Deep Learning?

The cost function refers to the measurement that is performed to find out the accuracy of the neural network with respect to the given sample and possible result. The value of the cost functioning can be easily calculated with the below mean square formula: –

MSE=1n∑i=0n(Y^i–Yi)^2

Here, Y^ and the expected value that need to be minimized.

Q4) What is the importance of the cost function?

The cost function is the measurement of the accuracy in the neural networks of deep learning. The concept provides performance as the whole value. When it comes to the deep learning the primary objective is to measure and minimize the cost function.

Q5) What is backpropagation?

Backpropagation can be defined as a training algorithm that is used for a deep neural network. In this method of training, the developer moves one error from the end of the network to the weight inside the network. Doing this makes the gradient more efficient.

Q6) What is gradient descent?

Generally, gradient descent is the optimized algorithm that is used to comprehend the value of the parameters that can be leveraged to minimize the cost function. The value can be computed using the below formula: –

Θ:=Θ–αd∂ΘJ(Θ)

Where Θ denotes the parameter vector, J(Θ) denotes cost function and α denotes the learning rate.

Q7) What are the types of gradient descent?

You can divide the gradient descent into three main categories: –

- Stochastic gradient descent
- Batch gradient descent
- Mini-batch gradient descent

Q8) What do you understand by the term Multi-Layer Perceptron (MLP)?

In the deep learning, Multi-Layer Perceptron (MLP) is the combination of the different layer; an outer layer, input layer, and hidden layer. Right opposite to the single-layer perceptron, multiple layer perception can classify the nonlinear classes.

Q9) What is data normalization? When do you use it?

Data normalization is the process that is performed to organize the data models which enhance the unity of the entity types. There are basically two primary advantages of the data normalization, first; increased consistency and the second is easier object data mapping.

Q10) Why do we use activation function in a neural network?

In the simplest terms, activation function analyses and take the decision of whether a neuron should be fired or not. The activation functions react upon the weighted sum of inputs and take it as an activation function. The most common examples of activation function include Softmax, Tanh, Sigmoid, and ReLU.

Q11) What is the primary role of the activation function?

The activation function plays a huge role in the neural network by helping it to comprehend more about the complex functions. In the absence of the activation function, the neural networks will not be able to learn about the non-linear functions.

Q12) What is the significance of Weight Initialization in Neural Networks?

There is a significant role of the weight initialization in the neural networks of deep learning. As they work as the activation function, a bad weight initialization can prevent the network from tracking the pattern and learning new things. Hence, having good weight initialization is necessary for quicker convergence.

Q13) What is the main difference between Deep learning, Machine learning, and AI?

There are many beginners who consider Machine learning, deep learning and AI as the very same thing same but in reality, they are far from each other. AI is the technology created to enable the machines to mimic human activities. Machine learning, on the other hand, is an aspect of AI that utilizes the statistical methods to enhance the experience. Lastly, deep learning is a subset of machine learning that uses multi-layer networks to make human-like decisions.

Q14) Define Hperparameteres and give a few examples

In deep learning, Hperparameteres are the key variables that are used to determine the network structure and how the whole networks are trained. It is basically set right before the training. Some of the most common examples of Hperparameteres includes activation function, learning rate, batch size, momentum, and batch size.

Q15) What is the dropout?

If put in simple terms, dropout is the drooping out units during the training phase. Here the term the “dropping” means these units were not considered during the forward or backward pass in the deep learning.

Q16) Does dropout prevent the overfitting? If yes, then how?

Yes, there is no denial to the fact that dropout prevents the overfitting through the layer’s “over-reliance” on its inputs. As the inputs are not always present there, the layers have learned to use all of the units.

Q17) Write down a few names of deep learning frameworks?

The most common names that define the frameworks of deep learning are Caffe, Keras, MXNet, TensorFlow, Torch/PyTorch, and Chainer. In this era of technology, the number is not limited to these frameworks as there are always more to come.

Q18) What are tensors?

The tensors are no more than a method of presenting the data in deep learning. If put in the simple term tensors are just multidimensional arrays that allow developers to represent the data in a layer which means deep learning you are using contains high-level data sets where each dimension represents a different feature.

Q19) What are the benefits of using tensors?

The foremost benefit of using tensors is it provides the much-needed platform-flexibility and is easy to trainable on CPU. Apart from this, tensors have the auto differentiation capabilities, advanced support system for queues, threads, and asynchronous computation. All these features also make it customizable.

Q20) What do you learn by the computational graph?

The computational graph is the method to represent the series of Tensorflow in the nodes in the math graph. Each of the nodes in the series takes from zero to ten tensors and produce a tensor as the output.

Q21) Describe the main layers of the CNN

The CNN is the combination of the four layers which are known as:

- Convolution: it is the set of the independent filters.
- ReLu: it is the key layer that is used with the convolution layer.
- Pooling: It refers to the function that is used to reduce the total number of parameters in the network.
- Full Connectedness: it has all the activation to the previous layer.

Q22) Define the concept of RNN?

RNN is the type of artificial neutral which were created with an objective to analyze and recognize the patterns in the sequences of the data. Due to their internal memory, RNN can certainly remember the things about the inputs they receive.

Q23) What are the most common issues faced with RNN?

Although, RNN is around for a while and use backpropagation, there are some common issues faced by developers who work it. Out of all some of the most common issues are:

- Exploding gradients
- Vanishing gradients

Q24) Explain the primary importance of LSTM?

LSTM which stands for Long short-term memory is the well-designed neural network architecture that is commonly used in deep learning. Unlike the other neural networks, LSTM can only be used to process the single side data points but when needed it can also process the entire sequences of the data.

Q25) What is an autoencoder?

Autoencoder is the advanced-level machine learning algorithm that is based on the backpropagation principle. It has basically hidden layers that describe codes that represent the input.

Q26) What are the principles layers in the autoencoder?

An autoencoder basically consists of three layers which are known as:

Q27) What are the variations in autoencoder?

The variations in the autoencoders can be described as below:

- Sparse Autoencoders
- Contractive Autoencoders
- Convolution Autoencoders
- Deep Autoencoders

Q28) Are there any limitations of Deep learning?

Just like any aspect of the technology, deep learning also has its fair share of limitations. Here are some of the limitations of deep learning: –

- It requires a large amount of training data.
- The neutral networks on which deep learning run can be easily fooled.
- So far, it doesn’t well integrate with prior knowledge.

Q29) Is it difficult to learn about deep learning?

No, with the proper guidance and supervision of professional trainers, candidates can easily learn about the concepts of deep learning.

Q30) Describe what do you understand by CNN?

The term CNN denotes a Convolutional neural network (CNN) which is the class of deep neural networks. Right opposite to the neutral networks, where input was vector here, in CNN neutral work as the visual imaginary.