AI0-001 AI Concepts and Techniques Practice Question
Which neural network architecture is specifically designed to handle sequential data and mitigate the vanishing gradient problem?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Long Short-Term Memory (LSTM) network
LSTM (Long Short-Term Memory) is a type of RNN designed with gating mechanisms to prevent vanishing gradients in long sequences. CNNs are for spatial data; vanilla RNNs suffer from vanishing gradients; transformers use attention but are not specifically designed to mitigate vanishing gradients (they use residual connections).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convolutional Neural Network (CNN)
Why it's wrong here
CNNs excel at spatial data like images, not sequential data with long-range dependencies.
- ✗
Transformer
Why it's wrong here
Transformers use self-attention and are effective for sequences, but they rely on residual connections and layer normalization to mitigate vanishing gradients, not a core design for that purpose.
- ✗
Vanilla Recurrent Neural Network (RNN)
Why it's wrong here
Vanilla RNNs suffer from vanishing gradients, making them poor at learning long-term dependencies.
- ✓
Long Short-Term Memory (LSTM) network
Why this is correct
LSTMs use forget, input, and output gates to control information flow, effectively handling long sequences and vanishing gradients.
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