What are deepfakes?

What is the purpose of the positional encoding in a transformer? Would you use positional encoding in an RNN?

Why shouldn’t we use the test set for hyperparameter tuning?

Part (e): What do you conclude about of such a system?

Errors in binary classification: Your classmate Charlie is a UF CS student who is doing undergraduate research at the Super Duper Awesome Deep Transformers Learning Lab (SDADTLL). Researchers at the lab have trained a neural network model (based on a powerful LLM) to predict the validity of any hypothesis formulated as an English sentence. Specifically, given any hypothesis such as: “job applicants who wear red on the day of the interview are 2.37% more likely to be hired than those who wear blue,” or “left-handed individuals are faster at reading nonfiction books than right-handed individuals,” the model outputs TRUE (+1) or FALSE (0). The network has 27651 layers and 7.1 quadrillion parameters and it was trained on 112 Exabytes of data. In this problem, you will help Charlie evaluate the performance of the model on real-world data.

Given a decision tree with depth d. What is the complexity of computing the prediction on a new feature vector?

Give two differences between SVM and k-Nearest Neighbors.

Part (a): What are the two neural network approximation theorems and what do they say?

List three different things you can try if your neural network is overfitted.

What is the difference between saliency maps and proxy models?