Deep Learning Interview Questions

If you are a deep learning engineer, you need to have a thorough understanding not only of coding but also of each of the components that go into creating a successful deep learning algorithm.

Example: "The primary function of a neural network is to receive a set of inputs, perform complex calculations and then use the output to solve the problem. A neural network is used for a range of applications. One example is classification; there are many classifiers available today, such as random forest, decision trees, support vector, logistic regression and so on, and of course neural networks."

Each sheet contains neurons called “nodes,” performing various operations. Neural Networks are used in deep learning algorithms like CNN, RNN, GAN, etc.

Following are some of the applications of deep learning :

Following are the advantages of neural networks :

Following are the disadvantages of neural networks :

Both shallow and deep networks are good enough and capable of approximating any function. But for the same level of accuracy, deeper networks can be much more efficient in terms of computation and number of parameters. Deeper networks can create deep representations. At every layer, the network learns a new, more abstract representation of the input.

Overfitting is the most common issue which occurs in deep learning. It usually occurs when a deep learning algorithm apprehends the sound of specific data. It also appears when the particular algorithm is well suitable for the data and shows up when the algorithm or model represents high variance and low bias.

Backpropagation can be divided into the following steps :