Posted on : 19 Apr, 2021, 06:25:12 PM
Created by : Somya Goswami
Deep learning is becoming one of the fastest-growing fields of the IT sector in the 21st century. It refers to well-covered skills and a set of technology that permits machines to predict outputs from layered input sets. Working with it required proper knowledge & efforts; deep learning skills are being embraced by organizations globally, which is prominently seen in the interviews.
Interview questions can sometimes get a bit tougher to answer. That’s why Wissenhive presented this blog named ‘Top 50 Deep Learning Interview Questions’ while putting together the most asked interview questions with answers by industry experts and professionals.
Deep learning refers to a part of machine learning that involves a large volume of structured, semi-structured, or unstructured data while using complex algorithms inspired by the brain’s function and structure to train neural networks. Deep learning also performs various complex operations to extract hidden features and patterns.
|Scope||Deep Learning||Machine Learning|
|Dependency on data||Performs exceptionally well on the large data set||Great performance on data set which are small to medium-sized|
|Dependency on Hardware||A high-end machine with top-notch configurations is required to assess data.||Low and machine can suffice|
|Time of Execution||It can take up to weeks as the process is long||Ranges from few minutes to certain hours|
|Feature Engineering||Understanding of best features to represent data is not required||Understanding of features is needed to represent the data|
|Interpretability||It is very difficult||Some algorithms can be interpreted easily, while some are impossible.|
|Scope||Supervised Learning||Unsupervised Learning|
|Deals with||Labeled data||Unlabeled data|
|Accuracy||Produces accurate results||Generates moderates results|
There are three algorithms that include supervised learning.
There are three algorithms that include supervised learning.
Perceptrons are very similar to the human brain’s neuron that receives and collects inputs from multiple entities and applies or provides inputs to those who transform them into output. It refers to an algorithm that is mainly used for performing supervised learning of binary classifiers. This algorithm is used in RNN, CNN, GAN, etc.
Forward propagation refers to a process where inputs are passed to the hidden layer and weights. In every hidden layer, the activation function’s output is calculated until the other layer is processed. The process begins with the input layer and moves towards the final layer of output.
Backpropagation refers to one of the training algorithms that is used for multilayer networks. It works on transferring the error data or information from the network end to all inside network weights. It is divided into multiple steps, and those are
There is a various application included in deep learning, but some of the popular applications are:
The answer to this question based on an individual’s knowledge and skill level so, make sure you answer this question based on your experience, but some of the top frameworks of deep learning are
The Boltzmann machine is one of the basic models of deep learning, which resembles a Multi-Layer Perceptron’s simplified version. This model features a hidden layer and a visible input layer which is the two-layer neural net that makes proper stochastic decisions to check whether a neuron should remain on or off. Nodes can connect across different layers, but two nodes that belong to the same layer can not connect.
An RBM or Restricted Boltzmann Machine refers to an undirected graphical model, which is a very popular algorithm in the deep learning field.
Overfitting refers to a very common issue that occurred while working with deep learning. Overfitting refers to a scenario where the deep learning algorithms check data to gain some valid information. It makes the model of deep learning picking up noise rather than valuable and useful data, causing very low bias and high variance while making the model less accurate.
Activation functions are deep learning entities that are used in translating inputs into a usable parameter output. It decides if neurons should be activated or not by calculating the actual weighted sum with the bias while making the model output non-linear.
There are various types of Activation function, and those are
There are few stages that are included in the building model, and those are
There are three different types of layers included in neural networks.
Fourier transform function or the package used to analyze, manage and maintain the large number of data presented in the database to take real-time array data and process them. It maintains and ensures high efficiency and makes the model open to process on multiple signals.
There are mainly five steps involved in deep learning for training a perception, and those are
It is used as an accuracy measurement function to check if the neutral network has accurately learned the training data or still learning by comparing the training dataset to the testing dataset. It is a primary performance measure of the neural network.
Gradient Descent refers to one of the most used optimal algorithms to minimize errors and maximize the cost function. The main aim behind Gradient Descent is to find the local-global minimum of the function and determine in providing direction to reduce the model’s error.
Three are three different types of variant included in gradient descent, and those are
|Scope||Batch Gradient Descent||Stochastic Gradient Descent|
|Usage of dataset||computes the gradient using the entire dataset.||Computes the gradient using a single sample.|
|Converge||It takes time to converge because of the huge data volume and slow updations of weights.||Converges faster than the batch because stochastic is more frequent in updating weight.|
Data normalization is a process of reforming and standardizing data which is the pre-processing step that eliminates redundancy in data, is used to rescale value to fit in a particular or specific range, and assures netter convergence in the backpropagation process.
A computation graph in deep learning refers to an operation series that helps take in the inputs and arrange them in the graph structure as nodes. Computation graphs can be considered as an implementation of mathematical calculations in a graph to help in high performance and parallel processes in terms of capability.
The cost function is also referred to as ‘’error’’ or ‘’loss’’, which focuses on evaluating the model’s performance and computing the output layer error during backpropagation. It also pushes error backward through the strong neural network and uses them during other training functions.
It is one of the self-gated activation functions that is designed and developed by Google. The mathematical formula of the swish function is
The Autoencoders refers to an artificial neural network that helps in learning without any supervision and direction. These networks have advanced ability to learn automatically through mapping the inputs in the corresponding outputs, including two entities.
There are four different types of autoencoders, and those are
There are wide uses of autoencoders, but some of the popular uses are
There are mainly five steps included while using the gradient descent algorithms.
|Scope||Single-layer perceptron||Multi-layer perceptron|
|Classifies||Cannot classify non-linear data||Classify non-linear data|
|Parameter amount||Limited amount of parameters||Withdraws loads of parameters|
|Efficiency||Less efficient||Highly efficient|
It refers to an activation function that is usually threshold-based. If the value of the input is below or above a specific threshold limit, the neuron is activated, then it sends a similar signal to the other layer and does not allow outputs that are multi-value outputs.
The full form of ReLU is a rectified linear activation unit, and it refers to a unit or node that implements the activation function. Usually, most of the networks use the rectifier function for the hidden layers that are referred to as a ratified network. It is considered one of the few milestones in deep learning fields or revolution.
Dropout in deep learning is a method that helps in avoiding overfitting a model. If the value of dropout is too low, it will leave a very minimal impact on learning. If the dropout value is too high, that means the model can under-learn, which causes lower efficiency.
There are numerous advantages of using Tensorflow, but some of the main benefits are
There are three different types of elements included in Tensorflow, and those are
There are many advantages of using an Array, and those are
The full form of CNN is convolutional neural networks that are used in performing images and visuals analysis. These neural network classes input a multi-channel image and work on it.
There are mainly four different types of layers presented in convolutional neural networks.
Pooling helps in reducing convolutional neural networks and spatial dimensions. It also advanced in performing operations like down-sampling to create a pooled feature map and reduce the dimensionality by sliding a filter matrix over the input matrix.
There are three reasons with makes mini-batch gradient popular, and those are
It is a scenario where the large models are trained on the dataset with huge data. This model is designed and used for simpler datasets that provide extremely efficient results and accurate neural networks. Some of the popular forms of transfer learning are
The full form of LSTM is Long-Short-Term Memory, one of the special kinds of recurrent neural networks capable of remembering information for a longer time and active in learning long-term dependencies as its default behavior. The three main steps of the LSTM network are
The full form of GANs is Generative Adversarial Networks which is used for achieving deep learning’s generative modeling. It refers to an unsupervised task that includes pattern discovery in the data for generating output. GANs mostly used in activities like
We, Wissenhive, hope you found the blog useful and helpful. The questions covered in this blog are the most sought-after interview questions for deep learning that will help the interviewee in acing their next interview!
If you are looking forward to learning and mastering all of the Deep learning or machine learning engineering skills & concepts and earning a certification in the same, do take a look at Wissenhive’s advanced and latest Deep learning-related certification offerings.