Top 10 Deep Learning Interview Questions And Answers | AI & Deep Learning Interview Questions

Analytics Vidhya β€’ February 12, 2024
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Analytics Vidhya

@analyticsvidhya

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For those of you, who are wondering what is β€œAnalytics Vidhya”, β€œAnalytics” can be defined as the science of extracting insights from raw data. The spectrum of analytics starts from capturing data and evolves into using insights/trends from this data to make informed decisions. β€œVidhya” on the other hand is a Sanskrit noun meaning β€œKnowledge” or β€œClarity on a subject”. Knowledge, which has been gained through reading literature or through self-practice / experimentation. Analytics Vidhya provides a community-based knowledge portal for Analytics and Data Science professionals. The aim of the platform is to become a complete portal serving all knowledge and career needs of Data Science Professionals. 7805

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The demand for skilled professionals well-versed in deep learning is at an all-time high. Organizations are understanding the power of artificial intelligence to derive insights from vast datasets. Data scientists equipped with deep learning expertise have become invaluable assets. ------------------------------------------------------------------------------------------------------- Top 10 Deep Learning Interview Questions for Data Scientists ------------------------------------------------------------------------------------------------------- 1. What is a neuron in a neural network? 2. What are the different types of data used in deep learning? 3. What are epochs and batches in deep learning training? 4. What is the difference between supervised and unsupervised learning in deep learning? 5. Explain the difference between activation functions like ReLU and sigmoid. When would you choose one over the other? 6. Describe the process of backpropagation in a neural network? 7. What are the different types of optimization algorithms used in deep learning? Which one is best for training convolutional neural networks (CNNs)? 8. What are the advantages and disadvantages of using dropout in deep learning models? 9. Explain the concept of overfitting and underfitting in deep learning. How can you prevent them? 10. What are the different types of regularization techniques used in deep learning? --------------------------------------------------------- Analytics Vidhya Community ❀️ --------------------------------------------------------- Know More πŸ”— https://bit.ly/4aoLGcK ---------------------------------------- Additional Resources πŸ”₯ ---------------------------------------- πŸ‘‰ Top 21 Python Interview Questions: https://youtu.be/IT9A6ZtR_9s πŸ‘‰ AI Tools to Build Resume: https://youtu.be/VF2D9hEV1cE πŸ‘‰ AI Tools to Build LinkedIn: https://youtu.be/nOUCLLem0-w πŸ‘‰ AI Tools to Get Resume Shortlisted: https://youtu.be/igkPBqyRluA πŸ‘‰ AI Tools to Build Cover Letter: https://youtu.be/qjyjdgw-SgQ πŸ‘‰ AI Tools Crack Interviews: https://youtu.be/0DLIVyXEKhQ πŸ‘‰ Data Analytics Interview Questions: https://youtu.be/PI1rTHLQ1ok πŸ‘‰ Top 10 SQL Projects: https://youtu.be/BlatyOOia04 πŸ‘‰ Top ML Projects: https://youtube.com/shorts/4Py7JiW-d-8 ---------------------------- Tags πŸ”₯ ---------------------------- #DeepLearningInterviewQuestions #NeuralNetworks #DataTypesInDeepLearning #TrainingEpochs #SupervisedLearning #UnsupervisedLearning #ActivationFunctions #BackpropagationExplained #OptimizationAlgorithms #DropoutInNeuralNetworks #OverfittingPrevention #UnderfittingPrevention #RegularizationTechniques #DeepLearningConcepts #NeuralNetworkTraining #MachineLearning #ArtificialIntelligence #DataScience #ComputerVision #NaturalLanguageProcessing #ConvolutionalNeuralNetworks #ModelTraining #AdvancedDeepLearning #DeepLearningTips #AIInterviewQuestions

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