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Dec 26, 2022
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NEW QUESTION 21
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?
- A. Reduce the learning rate and run the training process until the training loss stops decreasing.
- B. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.
- C. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
- D. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
Answer: A
NEW QUESTION 22
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen Which combination of algorithms would provide the appropriate insights? (Select TWO )
- A. The factorization machines (FM) algorithm
- B. The Random Cut Forest (RCF) algorithm
- C. The Latent Dirichlet Allocation (LDA) algorithm
- D. The k-means algorithm
- E. The principal component analysis (PCA) algorithm
Answer: D,E
Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.
NEW QUESTION 23
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Choose three.)
- A. Increase feature combinations.
- B. Increase dropout.
- C. Decrease regularization.
- D. Increase regularization.
- E. Decrease dropout.
- F. Decrease feature combinations.
Answer: A,D,E
NEW QUESTION 24
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