Common Deep Learning Interview Questions and Expert Answers

Last updated on Apr 24, 2023
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Deep Learning Interview Questions

Deep learning may be a machine learning technique that teaches computers to try to do what comes naturally to humans: learn by example. Deep learning may be a key technology behind driverless cars, enabling them to acknowledge a stop sign or to tell a pedestrian apart from a post. It's the perfection of vocal management in client devices like mobile phones, tablets, TVs, and speakers. Deep learning is obtaining considerable attention latterly and permanently. It's achieving results that weren't doable before. In deep learning, a pc model learns to perform classification tasks directly from pictures, text, or sound. Deep learning models can do progressive accuracy, typically prodigious human-level performance. Deep Learning Interview Questions are home to many fully-solved issues from various key topics. It's designed to perform interviews or exam-specific topics.

Models are trained by employing a giant set of labeled knowledge and neural network architectures that contain several layers. Deep learning achieves acceptance and correctness at higher standards than ever before. This helps client physical science meet user expectations, and it's crucial for safety-critical applications. Recent advances in deep learning have improved the purpose wherever deep learning outperforms humans in tasks like classifying objects in pictures.

Top 10 Deep Learning Interview Questions

Here in this article, we will be listing frequently asked Deep Learning Interview Questions and Answers with the belief that they will be helpful for you to gain higher marks. Also, to let you know that this article has been written under the guidance of industry professionals and covered all the current competencies.

Q1. What kind of a neural network will you use in deep learning regression via Keras-TensorFlow? Or How will you decide the best neural network model for a given problem?

The most important step in deciding on a neural network model is knowing your data well and choosing the best model. Also, it is important to consider whether the problem is linearly separable when deciding on a neural network model. As such, the task and data at hand play an important role in choosing the best neural network model for a given problem. However, unlike CNNs, LSTMs, or RNNs, which require a composition of nodes and layers, it is always recommended to start with simple models like Multilayer Perceptrons (MLP), which have only one hidden layer. Therefore, MLP is considered the simplest neural network. This is because weight initialization is unimportant, and we do not need to define the network structure in advance.

Q2. What kind of network would you prefer – an external or deep network for voice recognition?

Every neural network has a secret layer along with input and output layers. Neural networks that use one secret layer are called shallow neural networks, while those that use various hidden layers are called deep neural networks. Both shallow and deep networks are efficient in fitting into any function, but shallow networks need a lot of parameters, unlike deep networks that can fit functions even with a few parameters because of several layers. Deep networks are favored today over shallow networks because, at every layer, the model learns a story and abstract representation of the input. Also, they are much more capable regarding the number of parameters and calculations compared to shallow networks.

Q3. Why is dropout effective in deep networks?

The problem with deep neural networks is that training data with few examples are likely to be overloaded. Ensembling networks can reduce overfitting with different model configurations, but this requires additional effort to maintain multiple models and is computationally expensive. Dropout is one of the easiest and most successful ways to reduce dependencies and overcome overfitting problems in deep neural networks. Using the dropout regularization method, we use a single neural network model to resemble different network architectures by dropping nodes during training. It is considered an effective regularization method because it improves the generalization error and has a low computational cost.

Q4. Can you build deep learning models based solely on linear regression?

Yes, building deep networks using a linear function as the motivation function for every layer is possible if a linear equation represents the issue. However, an issue that is a construction of linear functions is a linear function. Therefore, nothing can be pulled off by implementing a deep network because adding more nodes to the network will not increase the predictive capability of the machine learning model.

NOTE: Deep learning interviews are tough. If you are not well-prepared, some coding questions can leave you frantically searching for an answer. So prepare yourself to face all the queries.

Q5. When training a deep learning model, you observe that the accuracy of the model decreases after a few epochs. How will you address this problem?

A decrease in the accuracy of a deep learning model after some epochs means that the model learns from the dataset's properties and does not consider the features. This is known as overfitting a deep learning model. Either dropout regularization or early termination can be used to solve this problem. As the formula suggests, stopping early stops further training of the deep learning model when we notice that the model's inaccuracy has decreased. Dropout regularization is dropping some nodes or output layers so that the remaining nodes have different weights.

Q6. Is it possible to calculate the learning rate for a model a priori?

For simple models, it may be possible to determine the optimal value of the learning rate a priori. However, it is impossible for complex models to compute the optimal learning rate through theoretical reasoning that can make accurate predictions. Observation and experience play an important role in defining the optimal learning rate

Q7. Is there any difference between neural networks and deep learning?

Ideally, there's no huge difference between deep learning networks and neural networks. Deep learning networks are neural networks. However, the design may be slightly more advanced than the Nineteen Nineties. The supply of hardware and computing resources has created it doable to implement them currently.

Deep learning is an essential element of data science, which includes statistics and predictive modeling. Understanding Deep learning coding interview questions are quite difficult, but with hard work and preparation, anyone can crack it easily.

Q8. What do you understand by learning rate in a neural network model? What happens if the learning rate is too high or too low?

The learning rate is one of the main configurable hyperparameters used in training neural networks. Choosing a learning rate is one of the most difficult aspects of training neural networks. A huge learning rate value means that the model needs few training eras and changes fastly. On the other hand, a small learning rate means that the model may take a long time to converge or may not converge and get stuck at a sub-optimal solution. Therefore, it is recommended to find a good learning rate value by trial and error rather than using a learning rate that is too low or too high.
NOTE: Deep learning interview is one of the hardest and most stressful. So you must go through all the required questions and answers about deep learning and focus on your goal.

Deep learning is an essential element of data science, which includes statistics and predictive modeling. Understanding Deep learning coding interview questions are quite difficult, but with hard work and preparation, anyone can crack it easily.

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