Common Deep Learning Interview Questions and Expert Answers

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.

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.

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.

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.

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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.

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

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.

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.