Podcast
Questions and Answers
Which type of neural network does LSTM belong to?
Which type of neural network does LSTM belong to?
- Deep Neural Network (DNN)
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN) (correct)
- Feedforward Neural Network (FNN)
What are the three gates in LSTM responsible for?
What are the three gates in LSTM responsible for?
- Input, output, and store
- Input, output, and forget (correct)
- Input, output, and remember
- Input, output, and update
What is the advantage of LSTM over vanilla RNN?
What is the advantage of LSTM over vanilla RNN?
- Faster training time
- Higher accuracy
- Better handling of long term dependencies (correct)
- Simpler architecture
What dataset was used in the tutorial?
What dataset was used in the tutorial?
What modifications were made to the code in this tutorial compared to the BERT Text Classification tutorial?
What modifications were made to the code in this tutorial compared to the BERT Text Classification tutorial?
Flashcards
What is a Recurrent Neural Network (RNN)?
What is a Recurrent Neural Network (RNN)?
A type of neural network suited for sequential data, capable of maintaining hidden states to capture temporal dependencies.
What is the purpose of LSTM gates?
What is the purpose of LSTM gates?
LSTM networks use input, output, and forget gates to control the flow of information, allowing them to selectively remember or discard information.
Why use LSTM over standard RNNs?
Why use LSTM over standard RNNs?
LSTM networks are better at capturing and utilizing information from distant time steps in a sequence, mitigating the vanishing gradient problem.
What is the REAL and FAKE News dataset?
What is the REAL and FAKE News dataset?
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What code modifications were made?
What code modifications were made?
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Study Notes
Neural Network Type
- LSTM (Long Short-Term Memory) networks are a type of Recurrent Neural Network (RNN).
Gates in LSTM
- LSTM includes three main gates:
- Input Gate: Regulates the amount of new information added to the cell state.
- Forget Gate: Determines which information from the cell state should be discarded.
- Output Gate: Controls what information from the cell state is sent to the next layer.
Advantages of LSTM
- LSTM networks address the vanishing gradient problem commonly faced by vanilla RNNs, enabling them to learn long-term dependencies effectively.
- LSTMs can remember information for longer periods, making them more suitable for tasks involving sequential data.
Dataset Used
- A specific dataset was utilized in the tutorial, although the name of the dataset was not specified.
Code Modifications
- The tutorial made specific alterations to the code compared to the BERT Text Classification tutorial, although exact modifications were not detailed.
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