20 Long Short-Term Memory Interview Questions and Answers

Long Short-Term Memory (LSTM) networks are a type of recurrent neural network that are well-suited for modeling sequence data. In an interview, you may be asked questions about how LSTM networks work and how they can be applied to different tasks. Reviewing these questions ahead of time can help you prepare your responses and feel confident on the day of your interview. In this article, we review some questions you may have during your job interview.

Long Short-Term Memory Interview Questions and Answers

Here are 20 commonly asked Long Short-Term Memory interview questions and answers to prepare you for your interview:

1. What are LSTMs?

LSTMs are a type of recurrent neural network that are designed to better handle long-term dependencies in data. LSTMs are able to remember information for longer periods of time than traditional RNNs, and as a result, can provide better predictions.

2. Can you explain the architecture of an LSTM network?

LSTM networks are a type of recurrent neural network that are designed to better handle long-term dependencies. They do this by introducing a memory cell that can remember information for long periods of time. LSTM networks are composed of a series of these memory cells, which are connected together.

3. How do you define the size of a hidden state in LSTM networks?

The size of the hidden state is defined by the number of neurons in the hidden layer.

4. Can you explain how the memory cell in an LSTM is implemented computationally?

The memory cell in an LSTM is implemented as a forget gate, an input gate, and an output gate. The forget gate controls how much information from the previous cell state is forgotten. The input gate controls how much new information from the current input is allowed into the cell state. The output gate controls how much information from the cell state is allowed to pass out to the next cell state.

5. What is the difference between vanilla RNNs and LSTMs?

The main difference between vanilla RNNs and LSTMs is that LSTMs are able to better remember long-term dependencies, while vanilla RNNs tend to forget them. This is due to the fact that LSTMs have a special type of memory cell that can retain information for longer periods of time, while vanilla RNNs only have a single layer of memory cells.

6. Is it possible to process long sequences using regular RNNs? If not, then why not?

No, it is not possible to process long sequences using regular RNNs. The reason for this is that regular RNNs suffer from the vanishing gradient problem, which means that as the sequence gets longer, the RNN will have a harder and harder time learning from it. This is why LSTMs were developed, which are able to overcome the vanishing gradient problem and thus can learn from long sequences.

7. Why don’t we use Long Short-Term Memory Networks for smaller datasets or problems?

The main reason why we don’t use Long Short-Term Memory Networks (LSTMs) for smaller datasets or problems is because they are much more computationally expensive than other types of neural networks. LSTMs are designed to handle long-term dependencies, which requires them to keep track of a lot of information. This makes them slower and more resource-intensive than other types of neural networks.

8. In what situations would you prefer to use LSTMs over simple Neural Nets?

LSTMs are well-suited for tasks that require remembering information over long periods of time, such as language translation or speech recognition. This is because LSTMs are able to store information in their long-term memory, which allows them to better keep track of context and maintain a consistent understanding of the task at hand.

9. What does the forget gate in an LSTM unit do?

The forget gate is responsible for determining which information from the previous time step should be forgotten and which should be kept. This is done by first calculating a forget gate vector, which is then used to element-wise multiply the previous cell state vector. This has the effect of zeroing out certain parts of the cell state vector, effectively forgetting certain information.

10. What is the purpose of peephole connections in LSTMs?

Peephole connections are used to help the LSTM cell keep track of long-term dependencies. By allowing the cell to directly access the previous cell state, the peephole connection allows the LSTM to better learn and remember patterns over extended periods of time.

11. What’s the significance of Gates in Recurrent Neural Networks?

The gates in a recurrent neural network help to control the flow of information through the network. The gates can be open or closed, and this determines whether or not information is allowed to pass through. The gates are important because they help to prevent the network from becoming overwhelmed with information and losing track of what is important.

12. What are Gated Recurrent Units (GRUs)?

GRUs are a type of recurrent neural network that is designed to better capture long-term dependencies in data. GRUs have two gates, a reset gate and an update gate, that control how much information from the past is forgotten or retained. GRUs have been shown to outperform traditional recurrent neural networks on a number of tasks.

13. What’s the difference between LSTMs and GRUs? Which one should be used more often?

LSTMs and GRUs are both types of recurrent neural networks (RNNs) that are used for processing sequential data. The main difference between the two is that LSTMs have a memory cell that helps them remember information for longer periods of time, while GRUs do not have this memory cell. Because of this, LSTMs are better suited for tasks that require remembering information over a long period of time, while GRUs are better for tasks that do not require remembering information for such a long time.

14. Can you give me some examples of real-world applications where LSTMs have been successfully deployed recently?

LSTMs have been used for a variety of tasks recently, including machine translation, image captioning, and voice recognition.

15. Can you explain the vanishing gradient problem?

The vanishing gradient problem is an issue that can occur when training deep neural networks. It is caused by the gradients of the error function becoming increasingly small as they are backpropagated through the layers of the network. This can eventually lead to the gradients becoming so small that they are effectively zero, and the network is unable to learn any further.

16. Can you explain what gating mechanisms are in context of LSTM units?

Gating mechanisms are what allow LSTM units to keep track of long-term dependencies. This is done by controlling the flow of information into and out of the cell state. The three main gates are the input gate, the forget gate, and the output gate.

17. What are backpropagation through time algorithms?

Backpropagation through time algorithms are used to train recurrent neural networks. These algorithms work by unrolling the recurrent neural network in time, and then training the network using standard backpropagation algorithms. This approach allows the recurrent neural network to learn from long-term dependencies, which is not possible using standard backpropagation algorithms.

18. What’s your understanding of information flow in recurrent neural networks?

Information flow in recurrent neural networks is the process by which information is passed through the network. This can be thought of as a kind of feedback loop, where information is passed from one node to the next and then back again. This process allows the network to learn from previous inputs and to make predictions about future inputs.

19. Are there any disadvantages associated with using LSTMs?

One potential disadvantage of using LSTMs is that they can be computationally intensive, and therefore may not be well suited for applications where real-time processing is required. Additionally, LSTMs may struggle with long-term dependencies, meaning that they may have difficulty learning patterns that span a large number of time steps.

20. Can you explain what the output function in an LSTM network does?

The output function in an LSTM network is responsible for generating the output of the network given the current input and the internal state of the network. This function is typically a fully connected layer followed by a softmax activation function.