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

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.
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Top 10 Deep Learning Interview Questions for Data Scientists
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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?
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[@Gopinath0626]

You missed to explain what is optimizers??

[@Analyticsvidhya]

You're right, optimizers were missed! In short, they guide deep learning models by adjusting parameters to minimize loss (think radio dial for best station). Key points: ➡️ Evaluate: Measure model performance with loss function. ➡️ Calculate: Determine how much to adjust each parameter. ➡️ Update: Adjust parameters based on calculations. ➡️ Repeat: Iterate until minimum loss is reached. Different optimizers exist, each with strengths and weaknesses (e.g., Gradient descent, Adam, RMSprop). Remember, this is just a starting point. Explore further to deepen your understanding!