All homeworks should be submitted through Gradescope.

Homework 1

Part 1: Hands-on excercise

Implementing sparse representations (50 points)

In this homework, your goal is to implement sparse word and document representations. Sparse representations are crucial in many natural language processing tasks, particularly when working with high-dimensional data like text.

You will need to log in to Colab with your Yale account to access the notebook. Then you can copy the notebook to your own account and start working on that.

You will be completing parts indicated with

    #
    # % -- Your Implementation -- %
    #

Or # TODO: Implement.

Then you will be submitting the completed notebook with all the outputs.

Please see the instructions and the notebook here: Colab-1.

Part 2

Handout (20 points)

Download the homework handout from the following link: Download.

For this part, you need to complete the homework in LaTeX and return the pdf solution. Further instructions are provided in the pdf.

Part 3: Hands-on excercise

Implementing the Word2Vec model (50 points)

The third part is implementation of a Word2Vec SkipGram model from scratch. A Colab notebook is provided to guide you through the process of implementing the model from scratch and training it on a toy data sample.

Then you will be submitting the completed notebook with all the outputs.

Please see the instructions and the notebook here: Colab-2.