Assignments
Homework01: (complete by Monday March 23, 2026)
Using four separate Quarto or shared Colab Notebooks produce your output from running the code from the examples and from the book Chapter 2.
At the top of your first page of each Notebook you should include Name, Class, Section, and homework assignment.
The header of your Quarto Notebooks should include
---
title: "Stat. 654 Homework 1"
author: "Your name"
date: "March 23, 2026"
toc: true
format:
html:
embed-resources: true
---
Submit: Submit four Quarto Notebooks or shared Colab Notebooks. You can share the Colab Notebooks with me by providing the links in Canvas. If you use Quarto Notebooks, you can submit both your .qmd and .html files. If you use Jupyter Notebooks, you can submit both your .ipynb and .html files. To have your work visible in Canvas your Notebooks need to be .html. I will not be running your .qmd or .ipynb files to view your work.
- Github:
- Template Quarto Notebooks:
- Read: Chapter 1 and Chapter 2
- Install R and RStudio, if you do not have them installed. Run the reticulate::install_python() to download and install a version of Python that is compatible with Tensorflow.
- Install the R package keras3 (for use with a CPU).
- Canvas Announcement:
When installing the R package keras3, you can use this website as a reference.
https://keras3.posit.co to an external site.
Please note that there should be only three steps
Install the package keras3
load the library, library(keras3)
Run this command to install the python virtual environment, install_keras(backend = “tensorflow”)
You should only run the install_keras() function one time. Do not put this command into an R code chunk in an R Quarto Notebook. This will likely cause problem because it will reinstall everything every time you Render the Notebook. You should run this command only once in the R Console.
- Problems:
- Run the cars Example.
- Run the concrete Example.
- Run the iris Example.
- Run the code in Chapter 2 related to the mnist data. You can download the code for the book from this Deep Learning with R Notebooks, Second Edition. Use the most recent code that is in the /code folder. Copy the code into separate code chunks and explain what the code is doing directly above each code chunk. Do not include the other code that is not related to the mnist data.