Deep Learning Interview Questions

Now, this can be answered in** two ways**. If you are on a phone interview, you cannot perform all the calculus in writing and show the interviewer. In such cases, it best to explain it as such:

Deep Learning goes right from the simplest data structures like lists to complicated ones like computation graphs.

The **softmax** function is used to calculate the probability distribution of the event over 'n' different events. One of the main advantages of using softmax is the output probabilities range. The range will be between 0 to 1, and the sum of all the probabilities will be equal to one. When the softmax function is used for multi-classification model, it returns the probabilities of each class, and the target class will have a high probability.

Swish is a new, self-gated activation function. Researchers at Google discovered the Swish function. According to their paper, it performs better than ReLU with a similar level of computational efficiency.

Autoencoder is an artificial neural network. It can learn representation for a set of data without any supervision. The network automatically learns by copying its input to the output; typically,internet representation consists of smaller dimensions than the input vector. As a result, they can learn efficient ways of representing the data. Autoencoder consists of two parts; an encoder tries to fit the inputs to the internal representation, and a decoder converts the internal state to the outputs.

Dropout is a cheap regulation technique used for reducing overfitting in neural networks. We randomly drop out a set of nodes at each training step. As a result, we create a different model for each training case, and all of these models share weights. It's a form of model averaging.

Tensors are nothing but a de facto for representing the data in deep learning. They are just multidimensional arrays, which allows us to represent the data having higher dimensions. In general, we deal with high dimensional data sets where dimensions refer to different features present in the data set.

A Boltzmann machine (also known as stochastic Hopfield network with hidden units) is a type of recurrent neural network. In a Boltzmann machine, nodes make binary decisions with some bias. Boltzmann machines can be strung together to create more sophisticated systems such as deep belief networks. Boltzmann Machines can be used to optimize the solution to a problem.

Some important points about Boltzmann Machine :

The loss function is used as a measure of accuracy to see if a neural network has learned accurately from the training data or not. This is done by comparing the training dataset to the testing dataset.

The loss function is a primary measure of the performance of the neural network. In Deep Learning, a good performing network will have a low loss function at all times when training.

Autoencoders are artificial neural networks that learn without any supervision. Here, these networks have the ability to automatically learn by mapping the inputs to the corresponding outputs.

Autoencoders, as the name suggests, consist of two entities :

* **Encoder :** Used to fit the input into an internal computation state

* **Decoder :** Used to convert the computational state back into the output