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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:
Forward propagation : The inputs are provided with weights to the hidden layer. At each hidden layer, we calculate the output of the activation at each node and this further propagates to the next layer till the final output layer is reached. Since we start from the inputs to the final output layer, we move forward and it is called forward propagation

Backpropagation : We minimize the cost function by its understanding of how it changes with changing the weights and biases in a neural network. This change is obtained by calculating the gradient at each hidden layer (and using the chain rule). Since we start from the final cost function and go back each hidden layer, we move backward and thus it is called backward propagation
Deep Learning goes right from the simplest data structures like lists to complicated ones like computation graphs.
Here are the most common ones :
List : An ordered sequence of elements (You can also mention NumPy ndarrays here)

Matrix : An ordered sequence of elements with rows and columns

Dataframe : A dataframe is just like a matrix, but it holds actual data with the column names and rows denoting each datapoint in your dataset. If marks of 100 students, their grades, and their details are stored in a dataframe, their details are stored as columns. Each row will represent the data of each of the 100 students

Tensors : You will work with them on a daily basis if you have ventured into deep learning. Used both in PyTorch and TensorFlow, tensors are like the basic programming unit of deep learning. Just like multidimensional arrays, we can perform numerous mathematical operations on them. Read more about tensors here

Computation Graphs : Since deep learning involves multiple layers and often hundreds, if not thousands of parameters, it is important to understand the flow of computation. A computation graph is just that. A computation graph gives us the sequence of operations performed with each node denoting an operation or a component in the neural network
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 :
* It uses a recurrent structure.
* It consists of stochastic neurons, which include one of the two possible states, either 1 or 0.
* The neurons present in this are either in an adaptive state (free state) or clamped state (frozen state).
* If we apply simulated annealing or discrete Hopfield network, then it would become a 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