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Top 140 Neural Networks Interview Questions
Entry Junior Mid Senior Expert
Neural Networks Theoretical Questions
Q1:
How many neurons do you need in the output layer if you want to classify email into spam or ham?
Entry
Q2:
How are Neural Networks modelled?
Entry
Entry
Junior
Q5:
Name some applications of Neural Networks?
Junior
Q6:
Can you list all the hyperparameters you can tweak in a basic Multi Layer Perceptron?
Junior
Junior
Q8:
Related To: Model Evaluation, Deep Learning
How to know whether your model is suffering from the problem of Exploding Gradients?
Related To: Model Evaluation, Deep Learning Junior
Junior
Q11:
Related To: Deep Learning
How to know whether your model is suffering from the problem of Vanishing Gradients?
Related To: Deep Learning Junior
Q12:
What is the difference between Forward Propagation and Backward Propagation?
Junior
Q13:
Related To: Deep Learning
What is the difference between Deep Learning and Artificial Neural Networks?
Related To: Deep Learning Junior
Junior
Q15:
What are Neural Networks?
Junior
Q16:
How do Neural Networks get the optimal Weights and Bias values?
Junior
Q17:
What is Backpropagation and how does it work?
Junior
Q18:
What are Loss Functions in Neural Networks?
Junior
Q19:
What is an Activation Function?
Junior
Q20:
What are the roles of an Activation Function?
Junior
Q21:
Related To: PyTorch
What's the difference between the forward() and backward() methods in PyTorch?
Related To: PyTorchforward() and backward() methods in PyTorch? Junior
Q22:
What’s Transfer Learning? How it’s useful?
Mid Q23:
Related To: Data Processing, Feature Engineering
How to choose the features for a Neural Network?
Related To: Data Processing, Feature Engineering Mid Mid Q25:
Related To: Deep Learning
What happens when you trade the Breadth of a Neural Network for the Depth?
Related To: Deep Learning Mid Q26:
What are Bidirectional RNN and what are its uses?
Mid Mid Mid Q29:
Related To: Optimisation
When you are Optimizing a Neural Network, does it make sense to combine both Momentum and Weight Decay to improve the performance?
Related To: Optimisation Mid Q30:
Related To: RNN
What's the difference between Traditional Feedforward Networks and Recurrent Neural Networks?
Related To: RNN Mid Q31:
Related To: Deep Learning
How does Randomized Connection Dropping affect the Deep Learning model?
Related To: Deep Learning Mid Mid Mid Mid Mid Q36:
Related To: RNN
What's the difference between Recurrent Neural Networks and Recursive Neural Networks?
Related To: RNN Mid Mid Q38:
What's the difference between LTSM and GRUs units?
Mid Q39:
Are there any problems when using Random Initialisation to initialise Weight and Bias? If yes, how to overcome it?
Mid Q40:
What's the difference between VALID and SAME padding?
Mid Mid Q44:
Related To: CNN
Name some advantages of using Convolutional Neural Networks vs Dense Neural Networks for image classification
Related To: CNN Mid Mid Mid Q47:
How to choose the Number of Layers and Nodes in a Feedforward neural network?
Mid Q48:
How would you change a neural network from regression to classification?
Mid Mid Q51:
Related To: Deep Learning
How can you convert a Dense Layer of a CNN into a Fully Convolutional Layer?
Related To: Deep Learning Mid Q52:
Related To: RNN
How many dimensions must the inputs of an RNN layer have? What does each dimension represent? What about its outputs?
Related To: RNN Mid Q53:
Should an Input Layer include a Bias Neuron?
Mid Q54:
Why is it a common practice to add a Pooling Layer after a CNN layer?
Mid Q55:
How would you tune the Training Algorithm Hyperparameters to get the highest accuracy in a Neural Network?
Mid Q56:
How would you tune the Network Structure (Model Design) Hyperparameters to get the highest accuracy in an Artificial Neural Network?
Mid Q57:
Related To: Model Evaluation
How would you prevent Overfitting when designing an Artificial Neural Network?
Related To: Model Evaluation Mid Q58:
Why should you Normalize the input for Neural Networks?
Mid Q59:
Related To: Computer Vision
How do Neural Networks distinguish useful features from non-useful features in Computer Vision?
Related To: Computer Vision Mid Q60:
Related To: Reinforcement Learning
What type of Neural Networks do Deep Reinforcement Learning use?
Related To: Reinforcement Learning Mid Mid Q62:
Explain why the Initialization process of weights and bias is important for NN?
Mid Q63:
Is it a good idea to use CNN to classify 1D signal?
1D signal? Mid Mid Q65:
How is One-Shot Learning still not attainable for Artificial Neural Networks?
Mid Q66:
What are some criticisms of Neural Networks?
Mid Q67:
Related To: NLP, Feature Engineering
How has Translation of words improved from the Traditional methods?
Related To: NLP, Feature Engineering Mid Mid Q69:
Why do you need to pad the input dataset when CNN is used?
pad the input dataset when CNN is used? Mid Mid Mid Mid Mid Mid Mid Q76:
What are some advantages of using Multilayer Perceptron over a Single-layer Perceptron?
Mid Q77:
What is the Vanishing Gradient Problem in Artificial Neural Networks?
Mid Q78:
What is the Exploding Gradient Problem in artificial Neural Networks?
Mid Q79:
Why does an artificial Neural Network use Backpropagation?
Mid Q80:
Related To: Deep Learning
Describe two ways to visualize features of a CNN in an image classification task
Related To: Deep Learning Mid Q83:
Related To: Random Forest, Ensemble Learning
Give some reasons to choose Random Forests over Neural Networks
Related To: Random Forest, Ensemble Learning Senior Senior Senior Senior Senior Senior Q90:
Related To: Deep Learning
Compare Hidden Markov Model vs Recurrent Neural Networks for solving sequence tasks
Related To: Deep Learning Senior Senior Q92:
What are the differences between Restricted Boltzmann Machine and the Feed-forward Network?
Senior Senior Q94:
Why are Recurrent Neural Networks known to be Turing Complete?
Senior Q95:
Related To: PyTorch
What's the differences between leaf and non-leaf variables in the context of a computational graph in PyTorch?
Related To: PyTorch Senior Q96:
What is a Radial Basis Function Network?
Senior Senior Senior Q99:
What is the difference between Dropout and Drop Connect?
Senior Q101:
How are Attention Metrics modeled in Neural Networks?
Senior Q102:
What is a GRU?
Senior Senior Q104:
What are some uses of Echo-State Networks?
Senior Senior Senior Senior Q108:
Related To: Deep Learning
Why does a Deep Neural Network work better than a Shallow Neural Network?
Related To: Deep Learning Senior Q109:
When do you use ReLU, Tanh, and Sigmoid activation functions in Neural Networks?
Senior Q110:
What is the difference between ReLU, Leaky ReLU, Exponential Linear Unit (ELU), and Parametric ReLU (PReLU)?
Senior Q111:
How would you fix the Exploding Gradient Problem?
Senior Senior Q113:
Related To: Decision Trees
What are the differences between Decision Trees and Neural Networks?
Related To: Decision Trees Senior Senior Senior Senior Q117:
Related To: Optimisation
Should Training Samples Randomly Drawn for Mini-Batch Training Neural Networks be drawn with or without Replacement?
Related To: Optimisation Senior Q118:
Related To: Optimisation
When you are Optimizing your Neural Network, is it a good idea to Prune the Network?
Related To: Optimisation Senior Senior Q120:
Related To: Anomaly Detection, Autoencoders
Explain how Autoencoders can be used for Anomaly Detection?
Related To: Anomaly Detection, Autoencoders Senior Senior Q122:
How to combine Categorical and Continuous input features for NN training?
Expert Expert Expert Q125:
Should Forecasting with Neural Networks be only treated as a Supervised Learning Problem?
Expert Q126:
Related To: ML Design Patterns, Model Evaluation, Data Processing
Are there any troubles when using Early Stopping?
Related To: ML Design Patterns, Model Evaluation, Data Processing Expert Q127:
What is a Deconvolutional Network?
Expert Q128:
How is Competitive Learning different from traditional Neural Networks?
Expert Q129:
Related To: Genetic Algorithms
What is NEAT (Neuroevolution of Augmenting Topologies) algorithm?
Related To: Genetic Algorithms Expert Q130:
What are Spiking Neural Networks?
Expert Expert Expert Q133:
Related To: Genetic Algorithms
How would you encode the structure of a Neural Network into a genome?
Related To: Genetic Algorithms Expert Neural Networks Practical Challenges
Q1:
Can you draw the architecture of a Perceptron with two input neurons, one bias neuron, and three output neurons?
Junior Q2:
Related To: Deep Learning, Keras
Using Keras, construct a simple autoencoder architecture using Dense layers and a Sequential model for 784-dimensional data
Related To: Deep Learning, Keras784-dimensional data Junior Mid Q4:
Related To: TensorFlow
Complete the code below to construct and evaluate a Convolutional Network in TensorFlow
Related To: TensorFlow Mid Mid Q6:
Can you sketch the LSTM cell's architecture?
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Azure Machine Learning (Azure ML) is a cloud-based service for creating and managing machine learning solutions. It’s designed to scale, distribute, and deploy machine learning models to the cloud. Follow along and learn the 23 most common Azure Mach...
Hadoop is an open-source big data processing framework. It leverages distributed computing to store and process large datasets in a fault-tolerant manner. According to recent reports, Apache Hadoop is one of the most sought-after big data skills with...
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Scala is a powerful language with functional programming capabilities that can be a good choice for data science, especially in big data and distributed computing scenarios. As an example, Apache Spark, a popular distributed data processing framework...
PyTorch popularity as a Deep Learning framework of choice is on the rise. As of December 2022, 62% of the academic papers were implemented in PyTorch whereas only 4% were for TensorFlow. Follow along and prepare effectively with these key 30 PyTorch ...
The use of Artificial Intelligence (AI) in machine learning and data science enabled advancements in areas such as natural language processing, computer vision, recommendation systems, fraud detection, predictive analytics, and personalized medicine....
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