Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
RNNs can process sequences of varying lengths due to their recurrent structure.
B
RNNs are inherently parallelizable, making them efficient for large datasets.
C
Long Short-Term Memory (LSTM) networks are a type of RNN designed to remember information over long sequences.
D
RNNs are primarily used for image classification tasks.
E
Gated Recurrent Units (GRUs) are simpler alternatives to LSTMs that can also manage long-range dependencies.
Understanding the Answer
Let's break down why this is correct
Answer
Recurrent Neural Networks are designed to process sequences by keeping a hidden state that carries information from earlier time steps, so they can handle variable‑length input and remember past context. They are trained with back‑propagation through time, which unfolds the network over the sequence and can lead to vanishing or exploding gradients. To mitigate these problems, variants such as LSTM and GRU add gating mechanisms that preserve long‑term dependencies. For example, an RNN can predict the next word in a sentence by updating its hidden state at each word and outputting a probability distribution over the vocabulary. Thus, statements that describe sequence handling, hidden‑state memory, training via BPTT, and the use of gated cells are true.
Detailed Explanation
RNNs can handle sequences of different lengths because they reuse the same weights over time. Other options are incorrect because People think RNNs are parallelizable, but they process one step after another; RNNs are not mainly for image classification; they excel at sequential data like text or speech.
Key Concepts
Recurrent Neural Networks (RNN)
Long Short-Term Memory (LSTM)
Gated Recurrent Units (GRU)
Topic
Recurrent Neural Networks (RNN)
Difficulty
easy level question
Cognitive Level
understand
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