12 Questions
What is the purpose of introducing gates in the hidden units of LSTM?
To stabilize the gradient flow
In the context of neural machine translation, what development was the Sequence-to-Sequence model designed for?
Neural machine translation
Why are deep neural networks not suitable for tasks where input/output sizes are not fixed?
Require fixed sized inputs/outputs
What is the main contribution of Seq-2-Seq architecture in utilizing LSTM for encoding/decoding?
Use of 2 LSTMs for encoding/decoding
What is the purpose of using distributional word embeddings like word2vec and GloVe in NLP?
Learn vector representations of words based on corpus statistics
Why is reversing the order of input tokens in Seq-2-Seq helpful?
Shorten paths between occurrence of word in input and output
What type of neural networks are commonly used in deep learning for tasks like machine translation and sentiment analysis?
Multi-Layer Perceptrons (MLP)
How do Recurrent Neural Networks (RNNs) handle sequential data?
RNNs use hidden states to pass information from previous inputs into the current calculation.
What is the purpose of using an 'encoder-decoder' architecture in RNNs?
To align inputs and outputs differently, such as in machine translation tasks.
Why are Recurrent Neural Networks (RNNs) difficult to train?
Due to issues like exploding or vanishing gradients.
What is the purpose of using 'softmax' vectors in generating text from Recurrent Networks?
To produce vectors of possible words for text generation.
How can more consistent results be achieved in text generation using Recurrent Networks?
By feeding the output back into the decoder.
Learn about multi-layer perceptrons (MLPs) and their applications in deep learning for tasks such as machine translation, language modeling, classification, and sentiment analysis. Understand how neural networks (NN) extract features and handle variable-length inputs.
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