chatGPT-experiment

I will add multiple experiments tried on the latest chatGPT by openAI

Experiment 1: Using ChatGPT for taking a Linkedin Assessment Exam for course

• Course Link: Advanced NLP with Python for Machine Learning
• Medium Blog post about this experiment: How I used ChatGPT to pass the Linkedin assessment exam
• Youtube video of entire experiment(unlisted): Testing ChatGPT on Linkedin Assessment Exam:

Questions

Questions Answered by ChatGPT:

1. Which NLP modeling technique uses simple document vectors to represent how important a word is to a document within the corpus?
2. Why must you convert the returned array from doc2vec into a list?
3. Which type of neural network contains only one hidden layer where all nodes in the hidden layer are connected to all nodes in both the input and output layers?
4. Why is TF-IDF important in vectorizing text?
5. When building your RNN, what is the first step?
6. Why is tokenization important when cleaning text data?
7. When does doc2vec compute sentences and documents?
8. What is the first action you should take that will make reading csv files into Python much easier?
9. Why is NLTK valuable?
10. What are the return types of the vector of doc2vec?
11. What is the second step after cleaning the data for the model?
12. Why do you need to average the word vectors for a sentence?
13. What is another term for sentence segmentation?
14. What is one option when using word2vec?
15. Why is Anaconda important in this course?
16. Why is word2vec important?
17. What is a good data skill to have for learning in this course?
18. What is the next step after printing data using the Random Forest Classifier?
19. Why are RNNs powerful for NLP problems?
20. Why is breaking code an important part of learning?
21. You want to use pad sequences to train your model. How do you construct a simple RNN model in code to use the pad sequences?
22. What is another important feature of word2vec in addition to word-to-vector conversion?

Completion certificate by ChatGPT