--- tags: hw0, handout --- # HW0 Programming: Warm-up :::info Conceptual section due **Friday, February 03, 2023 at 6:00 PM EST** Programming section due **Friday, February 03, 2023 at 6:00 PM EST** ::: Welcome to the first (programming) assignment of CSCI1470/2470! This assignment will guide you through the creation of a local environment for the course and ensure that you will be all set to start on Homework 1. **This assignment will be graded for completion.** ## Theme ![](https://i.imgur.com/2eZP32N.jpg) *An octopus doing assignment 0 (artist's rendition)* # Getting started Please click <ins>[here](https://classroom.github.com/a/w9rpAyb6)</ins> to get the stencil code. Reference this <ins>[guide](https://hackmd.io/gGOpcqoeTx-BOvLXQWRgQg)</ins> for more information about GitHub and GitHub Classroom. # Roadmap In order to complete (programming) assignments for this course, you will need a way to code, run, and debug your own Python code. While you are always free to use department machines for this (they have a pre-installed version of the course environment that every assignment has been tested against), you are also free to work on your own machines. Below we give you some information that is helpful for either of these situations. ## 1. Configuring Environoment ### Developing Locally In order to set up your virtual environment for this couse, we _highly recommend_ (and only formally support) the use of Anaconda to create and manage your Python environment. Once you have cloned the Github Classroom assignment for this assignment, you can do the following to setup your virtual environment: 1. Download **Anaconda** from [here](https://www.anaconda.com/products/distribution), and install it on your computer. :::info **Note:** If you have an existing Anaconda or Miniconda installation (such as from CS200), then you don't need to reinstall, and can just use that! You can tell if you have an existing install if the command `conda --version` is recognized. ::: :::warning **Windows**: When installing using the graphical installer, **be sure to check the box which adds `conda` to your `PATH`**. ::: 2. Navigate to the root of the cloned assignment in a terminal (such as the one in VSCode), and run `./env_setup/conda_create.sh`. This should set up a virtual environment named `csci1470` on your computer. **If you have an Apple M1, this script will be different.** (See below.) :::info You may need to restart your terminal after installing Anaconda in order for this to work. ::: :::warning **Note:** This might be slightly different depending on your platform: - **Apple M1**: We provide a slightly different script which you can call from `./env_setup/conda_create_m1.sh` for those who have Apple silicon. - **Windows**: If you are using Windows Powershell, then you can just run `./env_setup/conda_create.sh` (forward slashes), but if you are using *Command Prompt*, then you need to run `.\env_setup\conda_create.sh` (backslashes). **Some users have also experienced problems running the `*.sh` files entirely.** If this is the case, you can just open the `conda_create.sh` script in a text editor (such as VSCode), and run each line individually in your terminal. ::: 3. Run `conda activate csci1470`. **You will need to do this in every shell where you want to use the virtual environment**. :::warning If you are the above procedure doesn't work for you (although we **highly recommend trying to troubleshoot that first**), here is another method that does not rely on `conda` commands: :::spoiler 1. **Install Python 3.10** (or Python 3.9, we have not found any differences in functionality between them for our projects). 2. **Install the following packages** in either a virtual environment or your main python environment ipython==8.8.0 matplotlib==3.5.3 numpy==1.23.5 Pillow==9.4.0 scipy==1.9.3 tensorflow==2.11.0 tqdm==4.64.1 - We suggest you create a virtual environment. You can do so by running the following commands: 1. Create a folder where you plan to keep all your homework assignments. 2. In that folder, run `python -m venv cs1470`. This will create a new virtual environment caled `cs1470`. 3. Activate your virtual environment by running `cs1470/Scripts/activate` on Windows machines or `source cs1470/bin/activate` on Mac and Linux machines. Do this before starting any homework asssignment. 4. You can deactivate the virtual environment by typing `deactivate`. - You can install individual packages using `pip` commands (ie `pip install ipython==8.8.0`) - You can install a group of packages by pasting them into a requirements.txt file and running `pip install -r requirements.txt` ::: Once this is complete, you should have a local environment to use for the course! ### Department Machines :::info **Note:** Sometimes even if you set up your local environment correctly, you may experience unexpected bugs and errors that are unique to your local setup. To prevent this from hindering your ablity to complete assignments, we **highly recommend** that you familiarize yourself with the department machines, even if you expect to usually be working locally. ::: Department machines serve as a common, uniform way to work on and debug assignments. There are a variety of ways in which you can use department machines: 1. **In Person.** If you are in the CIT, you can (almost) always head into the Sunlab/[ETC] and work on a department machine. 2. **FastX**. FastX allows you to VNC into a department machine from your own computer, from anywhere! A detailed guide to getting FastX working on your own computer can be found [here](https://cs.brown.edu/about/system/connecting/fastx/). 3. **SSH**. The department machines can also be accessed by SSH (Secure Shell) from anywhere, which should allow you to perform command line activities (cloning repositories, running assignment code). You can check out an SSH guide [here](https://cs.brown.edu/about/system/connecting/ssh/). When using the department machines, you can activate the course virtual environment (which we have already installed) using: ``` source /course/cs1470/cs1470_env/bin/activate ``` Which will activate the course virtual environment. From here, you should be able to clone the repository (see a GitHub guide here for more information on using Git via the command line), and work on your assignment. :::info **Note**: Python files using `tensorflow` may require a little more time on startup to run on department machines (likely because it is pulling files from the department filesystem), but they should all run nonetheless. ::: ## 2. Test your environment Once you have configured your virtual environment (either running on a department machine or , run the file `test_network.py` by writing the following in your terminal: ```bash! python code/test_network.py ``` This will run a simple neural network using TensorFlow and save the model to a file in the `models/` folder, which we will use to check that you have your local environment working properly. ## 3. README For every assignment in this course, you will be required to submit a brief `README.md` file in your repository along with your code, explaining: 1. Any known bugs in your code, and how you have tried to fix them. 2. A breif explanation of your implementation. 3. Answers to any questions in the programming part of the assignment (**excluding _Conceptual Questions_**). For this assignment, **you should create a `README.md` file**, but since you didn't actually write any code for this assignment, feel free to either leave it blank or include a random message to the TAS :). # Handing In You should submit the assignment via Gradescope under the corresponding project assignment by zipping up your hw1 folder or through GitHub (recommended). To submit through GitHub, commit and push all changes to your repository to GitHub. You can do this by running the following three commands ([this](https://github.com/git-guides/#how-to-use-git) is a good resource for learning more about them): 1. `git add file1 file2 file3` - Alternatively, `git add -A` will stage all changed files for you. 3. `git commit -m “commit message”` 4. `git push` After committing and pushing your changes to your repo (which you can check online if you're unsure if it worked), you can now just upload the repo to Gradescope! If you’re testing out code on multiple branches, you have the option to pick whichever one you want. ![](https://i.imgur.com/fDc3PH9.jpg) # Conclusion Wohoo! You just completed your first assignment of CSCI1470/2470! :tada: :octopus: :tada: ::: success Just like how you are set up an environment to do your assignments, octopi like to set up territory in coastal marine waters, staying in small crevices in rocks and coral. ::: Now that your virtual environment is setup, you should be ready to do the rest of the assignments in this course.
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Brown Deep Learning Spring 2023 Handouts for CSCI1470/2470 @ Brown
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HW5 Conceptual: Image Captioning

Due April 7th at 6PM EST Answer the following questions, showing your work where necessary. Please explain your answers and work. :::info We encourage the use of $\LaTeX$ to typeset your answers, as it makes it easier for you and us, though you are not required to do so. ::: :::warning Do NOT include your name anywhere within this submission. Points will be deducted if you do so.

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HW4 Programming: Language Models

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Homework 0 Conceptual: Warm-up

:::info Conceptual section due Friday, February 3 at 6:00 PM EST Programming section due Friday, February 3 at 6:00 PM EST ::: Welcome to the first (conceptual) homework assignment of CSCI1470/2470! This assignment is just meant to be a short math review of concepts from Linear Algebra and Probability that you will need for this course, and also get you set up with a course virtual environment so that you will be ready to start the first programming assignment (Homework 1). First we'll introduce some starting concepts and ask you to expound on the details. While this doesn't have to be necessarily easy, it should prepare you for some of the early material and can be used to judge comfort with things that will come up a lot in the course. :::info

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HW4 Conceptual: Language Models

Due March 20th at 6PM Answer the following questions, showing your work where necessary. Please explain your answers and work. :::info We encourage the use of $\LaTeX$ to typeset your answers, as it makes it easier for you and us, though you are not required to do so. ::: :::warning Do NOT include your name anywhere within this submission. Points will be deducted if you do so.

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