Other Topics — Machine Learning Interview Questions
Introduction
Not interested in background on Recurrent Neural Networks? Skip to the questions here.
Machine Learning (ML) has done incredible things when it comes to Artificial Intelligence (AI). No one would question that in this day and age. One of the most unique and awe striking things ML has done is give us the ability to predict the future. The number of sales that may happen for the next 15 days, the way stocks would behave in the next month and many other things like that. This forecasting is one of the applications of Recurrent Neural Networks, RNNs. Henceforth, they are a perfect candidate for us to look into for interviews.
Article Overview
What are Recurrent Neural Networks?
RNNs (Recurrent Neural Networks) are a type of neural network that can be used to model sequence data. RNNs, which are derived from feedforward networks, behave similarly to human brains. RNNs are the only algorithm with an internal memory. Hence they can produce predicted outcomes with sequential data that other algorithms can’t.
What Makes Recurrent Neural Networks Important?
RNNs, as previously stated, can retain significant details about the input they receive, clearly thanks to their internal memory, which allows them to anticipate what will happen next with great precision. This is why they are the chosen algorithm for time series, speech, text, financial data, audio, video, weather, and many other types of sequential data. In comparison to other algorithms, RNNs can acquire a far more profound grasp of a sequence and its context. These use-cases make RNNs important.
Recurrent Neural Networks ML Interview Questions/Answers
As we have seen what Recurrent Neural Networks are and why they are essential, it is very much appropriate to move on to the interview questions related to them. Try to answer them in your head before clicking the arrow to reveal the answer.
Three-dimensional inputs are required for an RNN layer.
- The batch dimension (its size is the batch size) is the initial dimension.
- The second dimension is time (the size of which is the number of time steps)
- The third is the inputs at each time step (its size is the number of input features per time step).
The outputs are three-dimensional as well, with the same first two dimensions as the inputs, but the third dimension is equal to the number of neurons.
Stable gradients (exploding or vanishing) and a very limited short-term memory are the two key challenges while training RNNs. When working with extended sequences, both of these issues become worse.
To solve the problem of unstable gradients, we can:
- Reduce the learning rate
- At each time step, utilize a saturating activation function like the hyperbolic tangent (which is the default), as well as gradient clipping, Layer Normalization, or dropout
A Long Short-Term Memory layer or a Gated Recurrent Unit layer can be used to solve the problem of limited short-term memory.
RNNs work on the principle of preserving a layer’s output and feeding it back into the input in order to anticipate the layer’s output.
The RNN is a stateful neural network, which means it remembers information from previous layers as well as prior passes. As a result, this neuron is said to link between passes and across time.
Now because the RNN is stateful, the sequence of the inputs is important. Different arrangements of the same words provide different results.
RNN can be utilized in applications that are neither segmented nor connected, such as handwriting recognition or speech recognition.
The insertion of a loop at each node is the fundamental feature that distinguishes Recurrent Neural Networks (RNN) from other models. This loop is where RNNs get their recurrence mechanism. Each input is, as designed, given the same weight and sent to the network at the same time in a simple Artificial Neural Network (ANN). As a result, it would be difficult to capture the information that links “it” to “movie” in a statement like “I saw the movie and loathed it.”
The addition of a loop indicates that the previous node’s information will be preserved for the next node, and so on. This is why RNNs are far superior to ANNs for sequential data, and as text data is also sequential, they are an improvement.
Long short-term memory is abbreviated as LSTM. It’s a sort of RNN that’s used to sequence data. It is made up of feedback chains that allow it to function as a general-purpose computing entity.
When we employ RNNs, a scenario called vanishing gradient happens. Now because RNNs use back propagation, gradients will tend to get smaller as the network traverses backward iterations. This results in the model learning very slowly, which causes network efficiency issues.
Although one may not expect it, RNNs do actually come with their fair share of disadvantages too. Here are some:
- It has gradient vanishing and exploding issues
- It is quite tough to train an RNN.
- When utilizing tanh or relu as an activation function, RNNs cannot parse very long sequences.
Wrap Up
The advantages of RNNs and their usage in today’s world are evident from having a look at this article. Many key takeaways are present when one looks at their applications while also looking at their variations, such as LSTMs, and how the issues of RNNs can be resolved. The fact that RNNs work exceedingly well with text data is also something to think about and research. No wonder RNNs are something that anyone looking for a job in AI can be asked about!
