Deep learning is one of the fast-growing fields of information technology. It is a set of techniques that allows machines to anticipate outcomes from a layered set of inputs. Deep learning from scratch is being used by many big tech companies across the world which can create numerous job opportunities in the sector. If you are one of the readers wanting to start a career in deep learning? Then you need to know about the deep learning interview questions that are asked in the machine learning interviews. Let’s know more about it in this article.
Here are some of the top interview questions on deep learning
1 What is Deep Learning?
Deep learning involves taking large volumes of unstructured or structured data and using complex algorithms to train neural networks. It also helps in performing complex operations to extract hidden patterns and features such as differentiating the image of a cat from that of a dog. This is one of the most asked top deep learning interview questions for freshers to test their answering skills.
2 What is a Neural Network?
Neural networks can mimic the way humans learn, this is inspired by how the neurons in our brains fire, only much simpler. There are three types of neural networks: An input layer, A hidden layer, and an output layer.
3 Define Multi-Layer Perceptron?
As in neural networks, MLPs have three layers. It has the same structure as a single layer perceptron with one or many hidden layers. When we take a single layer perceptron, it can classify only linear separable classes with binary output (0,1), but in MLP it can classify nonlinear classes.
4 Tell us about Data Normalization and Why is it Important?
The process of standardizing and reforming data is known as ‘Data Normalization’. It’s a pre-processing step to delete data redundancy. As often, the data comes and you get the same information in various formats. In such cases, you should rescale values to fit into a particular space, reaching better convergence.
5 What is a Boltzmann Machine?
Boltzmann Machine is one of the most basic deep learning models that represents a simplified version of the multi-layer perceptron. The model features a visible input layer and a hidden layer-just a two-layer neural net that makes stochastic decisions as to whether a neuron should be on or off. If nodes are connected across layers, but no two nodes of the same layers are connected.
6 What does the Activation Function do in a Neural Network?
The Activation function decides whether a neuron should be fired or not. It accepts the weighted sum of inputs and bias as input to any activation function. Step function, ReLU, Sigmoid, Tanh, and Software are a few of the instances of the action functions. This is one of the top deep learning interview questions for experienced to test their practical skills in machine learning interviews.
7 What is the Cost Function?
A cost function is also referred to as loss or error. This is one of the deep learning interview questions to test your answering skills. It measures to evaluate how good your model’s performance is. It is used to compute the error of the end product layer during backpropagation. We push that error backward through the neural network and use that during the different training functions.
8 Explain about Gradient Descent?
Gradient Descent is an optimal algorithm to cut down the cost function or an error. The main aim is to find the local-global minima of a function. This determines the direction the model should take to reduce error. This is one of the deep learning interview questions to test your practical skills.
9 What is your Understanding of Backpropagation?
This is one of the widely and commonly asked deep learning interview questions. So, backpropagation is a method to improve the performance of the network, it backpropagates the error and updates the weights to decrease the errors.
10 Difference between Feedforward Neural Network and Recurrent Neural Network?
This is one of the deep learning questions, where the interviewee expects you to give a detailed and explained answer. A feedforward neural network signals travel in only one particular direction from input to output. There are no feedback loops, that the network considers only the currency input. Whereas, recurrent neural network’s signals travel in both directions, creating a looped network. This is one of the top deep learning interview questions to check your knowledge on differentiation in machine learning interviews.
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