Machine Learning Concepts and Techniques

This lesson introduces natural language processing (NLP) using Transformers, diverging from the fast.ai library's traditional RNN approach. It covers fine-tuning pre-trained models, specifically using the Hugging Face Transformers library. The lesson includes a walkthrough of a Kaggle competition, focusing on data preparation, tokenization, and understanding overfitting. It also discusses metrics, particularly the Pearson correlation coefficient, and concludes with ethical considerations in NLP.

Why is it beneficial to use more than one library for NLP?

• It helps in understanding different concepts in various ways.

• It reduces the complexity of the code.

• It makes the code run faster.

• It is a requirement for all deep learning courses.

What is the main purpose of fine-tuning a pre-trained model?

• To make the model run on different hardware.

• To increase the size of the dataset.

• To adjust the parameters that are uncertain and refine the confident ones.

• To completely change the model's architecture.

What was the first task performed using ULMFiT in the fast.ai course?

• Predicting the next word in a Wikipedia article.

• Classifying images from ImageNet.

• Determining the sentiment of IMDb movie reviews.

• Translating text from one language to another.

What is the primary challenge when using transformer-based approaches for long documents?

• They are not compatible with GPUs.

• They need to process the entire document at once, requiring significant memory.

• They cannot handle numerical data.

• They require a lot of labeled data.

In the Kaggle competition example, what is the purpose of the 'context' column?

• To provide additional metadata about the document.

• To store the labels for training the model.

• To categorize the patent and influence the similarity score between anchor and target.

• To indicate the length of the document.

What is a common issue with simpler models in machine learning?

• They are harder to interpret.

• They require more computational power.

• They are always more accurate.

• They tend to be systematically incorrect.

What is the primary purpose of a validation set in machine learning?

• To train the model.

• To store the final model.

• To measure the model's performance on unseen data.

• To visualize the data.

Why is it important to avoid using a random validation set for time series data?

• It increases computational complexity.

• It can lead to overfitting.

• It simplifies the model.

• It does not accurately represent future predictions.

What does the Pearson correlation coefficient measure?

• The complexity of a model.

• The similarity between two variables.

• The computational efficiency of a model.

• The difference between predicted and actual values.

Why should outliers not be removed without investigation?

• They are necessary for model validation.

• They always improve model accuracy.

• They simplify the data visualization.

• They can provide important insights about the data.