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Top 54 Deep Learning Interview Questions
Entry Junior Mid Senior Expert
Deep Learning Theoretical Questions
Q1:
What is the difference between Machine Learning and Deep Learning?
Entry
Q2:
What are Ensemble methods and how are they useful in Deep Learning?
Entry
Junior
Q4:
Related To: Model Evaluation, Neural Networks
How to know whether your model is suffering from the problem of Exploding Gradients?
Related To: Model Evaluation, Neural Networks Junior
Q5:
Related To: Neural Networks
How to know whether your model is suffering from the problem of Vanishing Gradients?
Related To: Neural Networks Junior
Q6:
Why would you use an Embedding Layer?
Junior
Q7:
How would you choose the Activation Function for a Deep Learning model?
Junior
Junior
Q9:
Why does the performance of Deep Learning improve as more data is fed to it?
Junior
Q10:
Related To: Neural Networks
What is the difference between Deep Learning and Artificial Neural Networks?
Related To: Neural Networks Junior
Q11:
Related To: Neural Networks
How does Randomized Connection Dropping affect the Deep Learning model?
Related To: Neural Networks
Mid Mid Mid Mid Mid Q16:
Related To: Neural Networks
How can you convert a Dense Layer of a CNN into a Fully Convolutional Layer?
Related To: Neural Networks Mid Q17:
Related To: Autoencoders
What's the difference between an Autoencoder and Variational Autoencoder?
Related To: Autoencoders Mid Q18:
Related To: Keras
What's the difference between concatenate and add layers in Keras and when would you use each one?
Related To: Kerasconcatenate and add layers in Keras and when would you use each one? Mid Mid Mid Q21:
What is multi-task learning and when should it be used?
Mid Q22:
Related To: Neural Networks
Describe two ways to visualize features of a CNN in an image classification task
Related To: Neural Networks Mid Q23:
Are there any problems when using Batch Normalization in Deep Neural Networks?
Mid Q24:
What's the difference between Batch Normalization, Instance Normalization and Layer Normalization?
Mid Mid Mid Q27:
What are Computation Graphs?
Mid Mid Q29:
Related To: Neural Networks
What happens when you trade the Breadth of a Neural Network for the Depth?
Related To: Neural Networks Mid Mid Mid Q32:
What is the difference between Linear Activation Function and Non-linear Activation Function?
Mid Q33:
What is the importance of using Non-linear Activation function?
Mid Q34:
What are the benefits of Pre-training?
Mid Mid Mid Q37:
What are the main difficulties when training GANs?
Senior Senior Q39:
Related To: Neural Networks
Compare Hidden Markov Model vs Recurrent Neural Networks for solving sequence tasks
Related To: Neural Networks Senior Senior Q41:
Related To: Neural Networks
Why does a Deep Neural Network work better than a Shallow Neural Network?
Related To: Neural Networks Senior Senior Q43:
What's the difference between Region-based Convolutional Neural Network (R-CNN), Fast R-CNN and Faster R-CNN?
Senior Senior Q45:
Related To: Curse of Dimensionality, Dimensionality Reduction
How does a Deep Neural Network escape/resist the Curse of Dimensionality?
Related To: Curse of Dimensionality, Dimensionality Reduction Senior Senior Q47:
How is Fourier Transform used to the benefit of Deep Learning?
Senior Q48:
What are the advantages of ReLU over Sigmoid function in Deep Neural networks?
Senior Senior Q50:
Suppose you want to train a classifier, and you have plenty of unlabeled training data but only a few thousand labeled instances. How would you proceed?
Expert Q51:
How GANs models are trained?
Expert Q52:
Can you name the main innovations in AlexNet, compared to LeNet-5? What about the main innovations in GoogLeNet and ResNet?
Expert Q53:
Related To: Genetic Algorithms
How can you optimise the architecture of a Deep Learning classifier using Genetic Algorithms?
Related To: Genetic Algorithms Expert Deep Learning Practical Challenges
Q1:
Related To: Keras, Neural Networks
Using Keras, construct a simple autoencoder architecture using Dense layers and a Sequential model for 784-dimensional data
Related To: Keras, Neural Networks784-dimensional data
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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...
Apache Spark is a unified analytics engine for large-scale data processing. It is built to handle various use cases in big data analytics, including data processing, machine learning, and graph processing. Follow along and learn the 23 most common an...
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....
Optimization algorithms are extensively used in training machine learning models. Data engineers employ algorithms like gradient descent, stochastic gradient descent, and variants (e.g., Adam, RMSprop) to optimize the model parameters and minimize th...
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