Podcast Beta
Questions and Answers
Which of the following is a limitation of RNN?
What are the solutions to overcome the limitations of RNN?
What is LSTM?
Which of the following is a type of RNN architecture designed to address the problem of vanishing gradients and inability to capture long-term dependencies in standard RNNs?
Signup and view all the answers
What is the main limitation of RNNs?
Signup and view all the answers
Which paper introduced the Transformers architecture in 2017?
Signup and view all the answers
Which of the following is NOT a limitation of RNN?
Signup and view all the answers
What is the purpose of LSTM in RNN architecture?
Signup and view all the answers
What is the difference between LSTM and standard RNN?
Signup and view all the answers
RNN is a type of neural network that is capable of capturing long-term dependencies
Signup and view all the answers
LSTM was suggested as a solution to the vanishing gradient problem in 1997
Signup and view all the answers
Transformers were introduced in the paper 'Attention is All You Need' by Schmidhuber et al. in 2017
Signup and view all the answers
RNN can capture long-term dependencies with ease
Signup and view all the answers
LSTM was introduced as a solution to the vanishing gradient problem in 1997
Signup and view all the answers
Transformers were introduced in the paper 'Attention is All You Need' by Vaswani et al. in 2017
Signup and view all the answers
Study Notes
Limitations of RNN
- RNNs have limitations, including the inability to capture long-term dependencies and vanishing gradients.
- RNNs are not capable of capturing long-term dependencies with ease.
Solutions to Overcome Limitations of RNN
- LSTM is a type of RNN architecture designed to address the problem of vanishing gradients and inability to capture long-term dependencies in standard RNNs.
- LSTM was introduced as a solution to the vanishing gradient problem in 1997.
LSTM
- LSTM is a type of RNN architecture.
Transformers
- The Transformers architecture was introduced in the paper 'Attention is All You Need' by Vaswani et al. in 2017.
- Note: It was not introduced by Schmidhuber et al. in 2017.
Purpose of LSTM in RNN Architecture
- The purpose of LSTM is to address the problem of vanishing gradients and inability to capture long-term dependencies in standard RNNs.
Difference between LSTM and Standard RNN
- LSTM is capable of capturing long-term dependencies, whereas standard RNNs are not.
Non-Limitations of RNN
- RNNs are a type of neural network.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Description
Test your knowledge on the limitations of RNN, including short-term memory and gradient issues, and learn about alternative solutions such as LSTM, GRU, and Transformers. This quiz is perfect for those interested in deep learning and natural language processing.
