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Deep Rob

Project 0

Overview

The objective of this project is to refresh your memory of working with the Python scripting languange and the PyTorch machine learning framework using the Google Colab development environment. In this project you will implement a collection of functions using core functionality of PyTorch Tensor objects. Through this project you will get an experience of working with the autograder that we will use for this course.

Instructions

  1. Download the project starter code
  2. Unzip the starter code and upload to Google Drive
    • Once unzipped, you should find a root directory titled ‘P0’. The ‘P0’ directory contains all starter code and files needed to complete this project. Please upload the ‘P0’ directory to your Google Drive.
  3. Open the *.ipynb and *.py files and implement features

    • We recommend implementing the features in a Google Colab environment. The Colab development environment can be accessed by double-clicking on each *.ipynb and *.py file within your Drive. Instructions for each feature are included in the pytorch101.ipynb file.

    • While working on the project, keep the following in mind:

      • The notebook and the python file have clearly marked blocks where you are expected to write code. Do not write or modify any code outside of these blocks.
      • Do not add or delete cells from the notebook. You may add new cells to perform scratch computations, but you should delete them before submitting your work.
      • Run all cells, and do not clear out the outputs, before submitting. You will only get credit for code that has been run.
      • To avoid experiencing Colab usage limits, save and close your notebooks once finished working.
  4. Submit your implementation for Autograder feedback
    • Once you have implemented a portion of the required features, you may submit your work for feedback from the Autograder. To receive feedback, download your *.ipynb and *.py files then upload them to the Project 0 Autograder. You may submit to the Autograder for feedback up to 5 times per day.
  5. Download final implementation
    • After implementing all features, save your work and download the completed *.ipynb and *.py files.
  6. Submit your python and notebook files for grading
    • Upload your files to the Autograder for grading consideration. No points given to this project.

Deadline

This project is due on Monday, September 16th at 11:59pm CT. We suggest starting as soon as possible.

Grading

This project will be graded by the Autograder. No points given to this project. You may submit to the Autograder for feedback up to 5 times per day.