diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 261d948..ad4e93f 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1,13 +1,13 @@ -# See here for image contents: https://github.com/microsoft/vscode-dev-containers/tree/v0.245.0/containers/python-3/.devcontainer/base.Dockerfile - -# [Choice] Python version (use -bullseye variants on local arm64/Apple Silicon): 3, 3.10, 3.9, 3.8, 3.7, 3.6, 3-bullseye, 3.10-bullseye, 3.9-bullseye, 3.8-bullseye, 3.7-bullseye, 3.6-bullseye, 3-buster, 3.10-buster, 3.9-buster, 3.8-buster, 3.7-buster, 3.6-buster -ARG VARIANT -FROM mcr.microsoft.com/vscode/devcontainers/python:0-${VARIANT} - -# Define the path to the virtualenv to work with -ARG VENV_PATH="/home/vscode/venv" - -COPY requirements.txt /tmp/pip-tmp/ -RUN su vscode -c "python -m venv /home/vscode/venv" \ - && su vscode -c "${VENV_PATH}/bin/pip --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/requirements.txt" \ +# See here for image contents: https://github.com/microsoft/vscode-dev-containers/tree/v0.245.0/containers/python-3/.devcontainer/base.Dockerfile + +# [Choice] Python version (use -bullseye variants on local arm64/Apple Silicon): 3, 3.10, 3.9, 3.8, 3.7, 3.6, 3-bullseye, 3.10-bullseye, 3.9-bullseye, 3.8-bullseye, 3.7-bullseye, 3.6-bullseye, 3-buster, 3.10-buster, 3.9-buster, 3.8-buster, 3.7-buster, 3.6-buster +ARG VARIANT +FROM mcr.microsoft.com/vscode/devcontainers/python:0-${VARIANT} + +# Define the path to the virtualenv to work with +ARG VENV_PATH="/home/vscode/venv" + +COPY requirements.txt /tmp/pip-tmp/ +RUN su vscode -c "python -m venv /home/vscode/venv" \ + && su vscode -c "${VENV_PATH}/bin/pip --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/requirements.txt" \ && rm -rf /tmp/pip-tmp \ No newline at end of file diff --git a/.devcontainer/add-notice.sh b/.devcontainer/add-notice.sh index 370bec1..7d87f53 100644 --- a/.devcontainer/add-notice.sh +++ b/.devcontainer/add-notice.sh @@ -1,19 +1,19 @@ -# Display a notice when not running in GitHub Codespaces - -cat << 'EOF' > /usr/local/etc/vscode-dev-containers/conda-notice.txt -When using "conda" from outside of GitHub Codespaces, note the Anaconda repository -contains restrictions on commercial use that may impact certain organizations. See -https://aka.ms/vscode-remote/conda/miniconda - -EOF - -notice_script="$(cat << 'EOF' -if [ -t 1 ] && [ "${IGNORE_NOTICE}" != "true" ] && [ "${TERM_PROGRAM}" = "vscode" ] && [ "${CODESPACES}" != "true" ] && [ ! -f "$HOME/.config/vscode-dev-containers/conda-notice-already-displayed" ]; then - cat "/usr/local/etc/vscode-dev-containers/conda-notice.txt" - mkdir -p "$HOME/.config/vscode-dev-containers" - ((sleep 10s; touch "$HOME/.config/vscode-dev-containers/conda-notice-already-displayed") &) -fi -EOF -)" - +# Display a notice when not running in GitHub Codespaces + +cat << 'EOF' > /usr/local/etc/vscode-dev-containers/conda-notice.txt +When using "conda" from outside of GitHub Codespaces, note the Anaconda repository +contains restrictions on commercial use that may impact certain organizations. See +https://aka.ms/vscode-remote/conda/miniconda + +EOF + +notice_script="$(cat << 'EOF' +if [ -t 1 ] && [ "${IGNORE_NOTICE}" != "true" ] && [ "${TERM_PROGRAM}" = "vscode" ] && [ "${CODESPACES}" != "true" ] && [ ! -f "$HOME/.config/vscode-dev-containers/conda-notice-already-displayed" ]; then + cat "/usr/local/etc/vscode-dev-containers/conda-notice.txt" + mkdir -p "$HOME/.config/vscode-dev-containers" + ((sleep 10s; touch "$HOME/.config/vscode-dev-containers/conda-notice-already-displayed") &) +fi +EOF +)" + echo "${notice_script}" | tee -a /etc/bash.bashrc >> /etc/zsh/zshrc \ No newline at end of file diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index ead47f1..66f302e 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -1,50 +1,50 @@ -// For format details, see https://aka.ms/devcontainer.json. For config options, see the README at: -// https://github.com/microsoft/vscode-dev-containers/tree/v0.245.0/containers/python-3 -{ - "name": "Python 3", - "build": { - "dockerfile": "Dockerfile", - "context": "..", - "args": { - // Update 'VARIANT' to pick a Python version: 3, 3.10, 3.9, 3.8, 3.7, 3.6 - // Append -bullseye or -buster to pin to an OS version. - // Use -bullseye variants on local on arm64/Apple Silicon. - "VARIANT": "3.8-bullseye", - // Options - "NODE_VERSION": "none" - } - }, - - // Configure tool-specific properties. - "customizations": { - // Configure properties specific to VS Code. - "vscode": { - // Set *default* container specific settings.json values on container create. - "settings": { - "python.defaultInterpreterPath": "/home/vscode/venv/bin/python", - "python.linting.enabled": true, - "python.linting.flake8Path": "/usr/local/py-utils/bin/flake8", - "python.linting.flake8Enabled": true - }, - - // Add the IDs of extensions you want installed when the container is created. - "extensions": [ - "ms-python.python", - "ms-python.vscode-pylance", - "GitHub.copilot" - ] - } - }, - - // Use 'forwardPorts' to make a list of ports inside the container available locally. - // "forwardPorts": [], - - // Use 'postCreateCommand' to run commands after the container is created. - // "postCreateCommand": "pip3 install --user -r requirements.txt", - - // Comment out to connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root. - "remoteUser": "vscode", - "features": { - "azure-cli": "latest" - } +// For format details, see https://aka.ms/devcontainer.json. For config options, see the README at: +// https://github.com/microsoft/vscode-dev-containers/tree/v0.245.0/containers/python-3 +{ + "name": "Python 3", + "build": { + "dockerfile": "Dockerfile", + "context": "..", + "args": { + // Update 'VARIANT' to pick a Python version: 3, 3.10, 3.9, 3.8, 3.7, 3.6 + // Append -bullseye or -buster to pin to an OS version. + // Use -bullseye variants on local on arm64/Apple Silicon. + "VARIANT": "3.8-bullseye", + // Options + "NODE_VERSION": "none" + } + }, + + // Configure tool-specific properties. + "customizations": { + // Configure properties specific to VS Code. + "vscode": { + // Set *default* container specific settings.json values on container create. + "settings": { + "python.defaultInterpreterPath": "/home/vscode/venv/bin/python", + "python.linting.enabled": true, + "python.linting.flake8Path": "/usr/local/py-utils/bin/flake8", + "python.linting.flake8Enabled": true + }, + + // Add the IDs of extensions you want installed when the container is created. + "extensions": [ + "ms-python.python", + "ms-python.vscode-pylance", + "GitHub.copilot" + ] + } + }, + + // Use 'forwardPorts' to make a list of ports inside the container available locally. + // "forwardPorts": [], + + // Use 'postCreateCommand' to run commands after the container is created. + // "postCreateCommand": "pip3 install --user -r requirements.txt", + + // Comment out to connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root. + "remoteUser": "vscode", + "features": { + "azure-cli": "latest" + } } \ No newline at end of file diff --git a/.github/workflows/metrics.yml b/.github/workflows/metrics.yml index f5719ae..bffa68a 100644 --- a/.github/workflows/metrics.yml +++ b/.github/workflows/metrics.yml @@ -1,30 +1,30 @@ -# GitHub Action to post GitHub metrics to an Azure Function App webhook -# Required secrets -# 1. A PAT with repo rights: PAT_REPO_REPORT -# 2. The webhook endpoint url: REPORTING_ENDPOINT_URL -# 3. The webhook endpoint key: REPORTING_ENDPOINT_KEY -# 4. Reporting group/team: REPORTING_GROUP - -name: "GitHub repo metrics report" - -on: - schedule: - # Run this once per day, towards the end of the day for keeping the most - # recent data point most meaningful (hours are interpreted in UTC). - - cron: "0 23 * * *" - workflow_dispatch: # Allow for running this manually. - -jobs: - report_metrics_job: - runs-on: ubuntu-latest - name: GitHub repo metrics report - steps: - - name: run github metrics image - id: github_metrics - uses: gloveboxes/GitHubMetricsAction@v1 - with: - github_repo: ${{ github.repository }} - github_personal_access_token: ${{ secrets.REPORTING_PAT }} - reporting_endpoint_url: ${{ secrets.REPORTING_ENDPOINT_URL }} - reporting_endpoint_key: ${{ secrets.REPORTING_ENDPOINT_KEY }} - reporting_group: $${{ secrets.REPORTING_GROUP }} +# GitHub Action to post GitHub metrics to an Azure Function App webhook +# Required secrets +# 1. A PAT with repo rights: PAT_REPO_REPORT +# 2. The webhook endpoint url: REPORTING_ENDPOINT_URL +# 3. The webhook endpoint key: REPORTING_ENDPOINT_KEY +# 4. Reporting group/team: REPORTING_GROUP + +name: "GitHub repo metrics report" + +on: + schedule: + # Run this once per day, towards the end of the day for keeping the most + # recent data point most meaningful (hours are interpreted in UTC). + - cron: "0 23 * * *" + workflow_dispatch: # Allow for running this manually. + +jobs: + report_metrics_job: + runs-on: ubuntu-latest + name: GitHub repo metrics report + steps: + - name: run github metrics image + id: github_metrics + uses: gloveboxes/GitHubMetricsAction@v1 + with: + github_repo: ${{ github.repository }} + github_personal_access_token: ${{ secrets.REPORTING_PAT }} + reporting_endpoint_url: ${{ secrets.REPORTING_ENDPOINT_URL }} + reporting_endpoint_key: ${{ secrets.REPORTING_ENDPOINT_KEY }} + reporting_group: $${{ secrets.REPORTING_GROUP }} diff --git a/.vs/VSWorkspaceState.json b/.vs/VSWorkspaceState.json index 1787cf6..9b97242 100644 --- a/.vs/VSWorkspaceState.json +++ b/.vs/VSWorkspaceState.json @@ -1,7 +1,7 @@ -{ - "ExpandedNodes": [ - "" - ], - "SelectedNode": "\\C:\\Users\\cacaste\\Source\\Repos\\Building-a-ML-demo-with-Copilot", - "PreviewInSolutionExplorer": false +{ + "ExpandedNodes": [ + "" + ], + "SelectedNode": "\\C:\\Users\\cacaste\\Source\\Repos\\Building-a-ML-demo-with-Copilot", + "PreviewInSolutionExplorer": false } \ No newline at end of file diff --git a/README.md b/README.md index 5f9f791..211dff4 100644 --- a/README.md +++ b/README.md @@ -1,76 +1,76 @@ -# Learning AI with GitHub Copilot - -Welcome! This repository hosts all the demos that were shown in the [*Learning AI with GitHub Copilot*](https://learn.microsoft.com/shows/learning-ai-with-github-copilot/?WT.mc_id=academic-88213-cacaste) video series, with instructions on how to replicate them on your own. -**Click on the image below to watch the series on demand.** - -
- -[![Learning AI with GitHub Copilot](./assets/learn-ai-with-copilot.png)](https://learn.microsoft.com/shows/learning-ai-with-github-copilot/?WT.mc_id=academic-88213-cacaste) - -
- -*Learning AI with GitHub Copilot* is a series designed for machine learning beginners that would like to get started with Python. It's an exciting learning journey, spanning from supervised learning fundamentals and scikit-learn framework to more advanced topics like computer vision and Pythorch framework. Also, you'll be meeting a special friend along the way, who will help you navigate the AI world. - -## Requirements - -Before you can run the demos included in this repository, you will need to have the following software installed on your system: - -- [VSCode](https://code.visualstudio.com/?WT.mc_id=academic-88213-cacaste) -- [Github](https://www.github.com/?WT.mc_id=academic-88213-cacaste) -- [GitHub Copilot](https://copilot.github.com/?WT.mc_id=academic-88213-cacaste) -- [GitHub Copilot Labs](https://githubnext.com/projects/copilot-labs/?WT.mc_id=academic-88213-cacaste) -- [Github Codespaces](https://github.com/features/codespaces?WT.mc_id=academic-88213-cacaste) - -## Instructions to Acquire GitHub Copilot Access - -GitHub Copilot is an AI-powered coding assistant that helps you write better code faster. To use GitHub Copilot for free, you can request access to the trial. - -To apply for access to the trial go to the [GitHub Copilot website](https://copilot.github.com/?WT.mc_id=academic-88213-cacaste) and follow the instructions. Once you have been granted access to the trial, you can install the [GitHub Copilot extension](https://aka.ms/get-copilot?WT.mc_id=academic-88213-cacaste) and the [GitHub Copilot Labs extension](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot-labs&?WT.mc_id=academic-88213-cacaste) in VSCode. - -**GitHub Copilot is free for verified students and teachers.** To apply for the free student and teacher plan, go to the [GitHub Education website](https://education.github.com/?WT.mc_id=academic-88213-cacaste) and follow the instructions. - -## Instructions to Acquire GitHub Codespaces Access - -GitHub Codespaces is a remote virtual development environment hosted in the cloud that you can customize with your pre-requisites using dev container configuration files. This creates a consistent and reproducible development environment for any user of your project. - -Every GitHub user have access to a free Codespaces plan. To learn more about quotas and limits of your plan, check on your own GitHub account [settings](https://github.com/settings/billing). You can use GitHub Codespaces on your browser or within VSCode. To use GitHub Codespaces in VSCode, you will need to install the [GitHub Codespaces extension](https://github.com/features/codespaces?WT.mc_id=academic-88213-cacaste). - -To open this repository in GitHub Codespaces on your browser click the button below: - -[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=630639684) - -If you are a student or an educator you may request additional benefits on GitHub Codespaces. To learn more about the GitHub Codespaces for students and educators, check the [GitHub Education website](https://education.github.com?WT.mc_id=academic-88213-cacaste). - - -## How to Download VSCode - -VSCode is a free, open-source code editor developed by Microsoft. To download VSCode, follow these steps: - -1. Go to the [VSCode website](https://code.visualstudio.com/?WT.mc_id=academic-88213-cacaste). -2. Click on the "Download" button for your operating system (Windows, macOS, or Linux). -3. Once the download is complete, run the installer and follow the instructions to install VSCode on your computer. - -Once you have installed VSCode, you can download the the [GitHub Copilot extension](https://aka.ms/get-copilot) extension from the VSCode marketplace and start using them to write code in Python and other programming languages. - -## Demos - -1. [Machine Learning Fundamentals](./demos/ml-fundamentals.ipynb) -2. [Classification Demo with Scikit-Learn](./demos/classification-demo.ipynb) -3. [Computer Vision Fundamentals](./demos/cv-fundamentals.ipynb) -4. [Computer Vision with PyTorch](./demos/cv-with-pytorch.md) - -Demos include the prompts that were used to generate the code with GitHub Copilot. Watch the video series to see the results of the code generation process. -If by replicating the demo you obtain different results, this is expected because GitHub Copilot is based on [OpenAI Codex](https://openai.com/blog/openai-codex?WT.mc_id=academic-88213-cacaste), a Large Language Model whose outputs are not deterministic. - -## Authors - -- [Carlotta Castelluccio](https://www.linkedin.com/in/carlotta-castelluccio/) -- [Gustavo Cordido](https://www.linkedin.com/in/gcordidoa/) - - -## Useful Resources - -Deep dive into the topics explored in this series: -- [Machine Learning for beginners curriculum](https://github.com/microsoft/ML-For-Beginners?WT.mc_id=academic-88213-cacaste) -- [Artificial Intelligence for beginners curriculum](https://github.com/microsoft/AI-For-Beginners?WT.mc_id=academic-88213-cacaste) - +# Learning AI with GitHub Copilot + +Welcome! This repository hosts all the demos that were shown in the [*Learning AI with GitHub Copilot*](https://learn.microsoft.com/shows/learning-ai-with-github-copilot/?WT.mc_id=academic-88213-cacaste) video series, with instructions on how to replicate them on your own. +**Click on the image below to watch the series on demand.** + +
+ +[![Learning AI with GitHub Copilot](./assets/learn-ai-with-copilot.png)](https://learn.microsoft.com/shows/learning-ai-with-github-copilot/?WT.mc_id=academic-88213-cacaste) + +
+ +*Learning AI with GitHub Copilot* is a series designed for machine learning beginners that would like to get started with Python. It's an exciting learning journey, spanning from supervised learning fundamentals and scikit-learn framework to more advanced topics like computer vision and Pythorch framework. Also, you'll be meeting a special friend along the way, who will help you navigate the AI world. + +## Requirements + +Before you can run the demos included in this repository, you will need to have the following software installed on your system: + +- [VSCode](https://code.visualstudio.com/?WT.mc_id=academic-88213-cacaste) +- [Github](https://www.github.com/?WT.mc_id=academic-88213-cacaste) +- [GitHub Copilot](https://copilot.github.com/?WT.mc_id=academic-88213-cacaste) +- [GitHub Copilot Labs](https://githubnext.com/projects/copilot-labs/?WT.mc_id=academic-88213-cacaste) +- [Github Codespaces](https://github.com/features/codespaces?WT.mc_id=academic-88213-cacaste) + +## Instructions to Acquire GitHub Copilot Access + +GitHub Copilot is an AI-powered coding assistant that helps you write better code faster. To use GitHub Copilot for free, you can request access to the trial. + +To apply for access to the trial go to the [GitHub Copilot website](https://copilot.github.com/?WT.mc_id=academic-88213-cacaste) and follow the instructions. Once you have been granted access to the trial, you can install the [GitHub Copilot extension](https://aka.ms/get-copilot?WT.mc_id=academic-88213-cacaste) and the [GitHub Copilot Labs extension](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot-labs&?WT.mc_id=academic-88213-cacaste) in VSCode. + +**GitHub Copilot is free for verified students and teachers.** To apply for the free student and teacher plan, go to the [GitHub Education website](https://education.github.com/?WT.mc_id=academic-88213-cacaste) and follow the instructions. + +## Instructions to Acquire GitHub Codespaces Access + +GitHub Codespaces is a remote virtual development environment hosted in the cloud that you can customize with your pre-requisites using dev container configuration files. This creates a consistent and reproducible development environment for any user of your project. + +Every GitHub user have access to a free Codespaces plan. To learn more about quotas and limits of your plan, check on your own GitHub account [settings](https://github.com/settings/billing). You can use GitHub Codespaces on your browser or within VSCode. To use GitHub Codespaces in VSCode, you will need to install the [GitHub Codespaces extension](https://github.com/features/codespaces?WT.mc_id=academic-88213-cacaste). + +To open this repository in GitHub Codespaces on your browser click the button below: + +[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=630639684) + +If you are a student or an educator you may request additional benefits on GitHub Codespaces. To learn more about the GitHub Codespaces for students and educators, check the [GitHub Education website](https://education.github.com?WT.mc_id=academic-88213-cacaste). + + +## How to Download VSCode + +VSCode is a free, open-source code editor developed by Microsoft. To download VSCode, follow these steps: + +1. Go to the [VSCode website](https://code.visualstudio.com/?WT.mc_id=academic-88213-cacaste). +2. Click on the "Download" button for your operating system (Windows, macOS, or Linux). +3. Once the download is complete, run the installer and follow the instructions to install VSCode on your computer. + +Once you have installed VSCode, you can download the the [GitHub Copilot extension](https://aka.ms/get-copilot) extension from the VSCode marketplace and start using them to write code in Python and other programming languages. + +## Demos + +1. [Machine Learning Fundamentals](./demos/ml-fundamentals.ipynb) +2. [Classification Demo with Scikit-Learn](./demos/classification-demo.ipynb) +3. [Computer Vision Fundamentals](./demos/cv-fundamentals.ipynb) +4. [Computer Vision with PyTorch](./demos/cv-with-pytorch.md) + +Demos include the prompts that were used to generate the code with GitHub Copilot. Watch the video series to see the results of the code generation process. +If by replicating the demo you obtain different results, this is expected because GitHub Copilot is based on [OpenAI Codex](https://openai.com/blog/openai-codex?WT.mc_id=academic-88213-cacaste), a Large Language Model whose outputs are not deterministic. + +## Authors + +- [Carlotta Castelluccio](https://www.linkedin.com/in/carlotta-castelluccio/) +- [Gustavo Cordido](https://www.linkedin.com/in/gcordidoa/) + + +## Useful Resources + +Deep dive into the topics explored in this series: +- [Machine Learning for beginners curriculum](https://github.com/microsoft/ML-For-Beginners?WT.mc_id=academic-88213-cacaste) +- [Artificial Intelligence for beginners curriculum](https://github.com/microsoft/AI-For-Beginners?WT.mc_id=academic-88213-cacaste) + diff --git a/demos/classification-demo.ipynb b/demos/classification-demo.ipynb index c9f5196..b2f5ddb 100644 --- a/demos/classification-demo.ipynb +++ b/demos/classification-demo.ipynb @@ -10,9 +10,16 @@ "To run and replicate this demo, leverage Copilot's assistance by pressing the `ENTER` key after each comment, followed by `Tab` when the Copilot suggestion appears on the screen. Once the cells are complete, just run them in order." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#import raw csv data" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -23,35 +30,187 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " CulmenLength CulmenDepth FlipperLength BodyMass Species\n", + "0 39.1 18.7 181.0 3750.0 0\n", + "1 39.5 17.4 186.0 3800.0 0\n", + "2 40.3 18.0 195.0 3250.0 0\n", + "3 NaN NaN NaN NaN 0\n", + "4 36.7 19.3 193.0 3450.0 0\n", + "5 39.3 20.6 190.0 3650.0 0\n", + "6 38.9 17.8 181.0 3625.0 0\n", + "7 39.2 19.6 195.0 4675.0 0\n", + "8 34.1 18.1 193.0 3475.0 0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# printing the first 5 rows of the dataset" + "# printing the first 5 rows of the dataset\n", + "df.head(9)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Let's visualize the distribution of the penguins species with a bar plot in matplotlib" + "# Let's visualize the distribution of the penguins species with a bar plot in matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.countplot(x=\"Species\", data=df)\n", + "plt.show()\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Let's visualize with boxplots how the FlipperLength, CulmenLength and CulmenDepth are distributed for each species\n", - "# importing seaborn" + "sns.boxplot(x=\"Species\", y=\"FlipperLength\", data=df)\n", + "plt.show()\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -84,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -106,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +291,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.16" + "version": "3.8.17" }, "orig_nbformat": 4 }, diff --git a/demos/cv-fundamentals.ipynb b/demos/cv-fundamentals.ipynb index 650c924..9d87a92 100644 --- a/demos/cv-fundamentals.ipynb +++ b/demos/cv-fundamentals.ipynb @@ -1,83 +1,83 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Computer Vision Fundamentals\n", - "\n", - "By using the provided questions, we can leverage GitHub Copilot's assistance in answering (and learning) the topic at hand. Pressing `ENTER` at the end of each question, followed by pressing the `Tab` key once the CoPilot suggestion pops up, will carry out the demo as shown in the videos." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What is Computer Vision?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Q: What is a basic definition of Computer Vision?\n", - "\n", - "#Q: And how are machine learning and AI used in Computer Vision?\n", - "\n", - "#Q: What machine learning structure is most commonly used in Computer Vision?" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### What is a Neural Network?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Q: What is a Neural Network?\n", - "\n", - "#Q: What differentiates a convolutional neural network from a regular neural network?\n", - "\n", - "#Q: What tools are most commonly used to work with Convolutional Neural Networks?" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### PyTorch" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Q What is PyTorch?\n", - "\n", - "#Q: What is a tensor?" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Computer Vision Fundamentals\n", + "\n", + "By using the provided questions, we can leverage GitHub Copilot's assistance in answering (and learning) the topic at hand. Pressing `ENTER` at the end of each question, followed by pressing the `Tab` key once the CoPilot suggestion pops up, will carry out the demo as shown in the videos." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is Computer Vision?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Q: What is a basic definition of Computer Vision?\n", + "\n", + "#Q: And how are machine learning and AI used in Computer Vision?\n", + "\n", + "#Q: What machine learning structure is most commonly used in Computer Vision?" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What is a Neural Network?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Q: What is a Neural Network?\n", + "\n", + "#Q: What differentiates a convolutional neural network from a regular neural network?\n", + "\n", + "#Q: What tools are most commonly used to work with Convolutional Neural Networks?" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### PyTorch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#Q What is PyTorch?\n", + "\n", + "#Q: What is a tensor?" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/demos/cv-with-pytorch.md b/demos/cv-with-pytorch.md index f77f581..9666205 100644 --- a/demos/cv-with-pytorch.md +++ b/demos/cv-with-pytorch.md @@ -1,54 +1,54 @@ -# Computer Vision with PyTorch - -Requirements: - -- Access to GitHub Copilot. -- GitHub Copilot Labs Extension installed. -- PyTorch. - -## Steps to replicate/test - -### 1. Installing PyTorch - -You can install PyTorch by following these steps: - -1. First, check which version of PyTorch is compatible with your system configuration and Python version by visiting the official [PyTorch](https://pytorch.org/get-started/locally/) website. -2. Once you have determined the correct version of PyTorch to install, you can use pip to install it. Open your terminal or command prompt and enter the following command: - -```bash -pip install torch torchvision -``` - -This will install the latest stable version of PyTorch and its related package torchvision. - -Alternatively, if you have a specific version of PyTorch that you want to install, you can specify the version number in the pip command. For example, to install PyTorch version 1.9.0, you can use the following command: - -```bash -pip install torch==1.9.0 torchvision==0.10.0 -``` - -3. Once the installation is complete, you can verify that PyTorch is installed correctly by opening a Python interpreter and importing the torch module: - -```python -import torch - -# Test PyTorch installation -print(torch.__version__) -``` - -If PyTorch is installed correctly, you should see the version number printed to the console. - -## 2. Download the *Transfer Learning for Computer Vision* notebook from the PyTorch Website - -Head to the [PyTorch Tutorials](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) website, and click on either **Download Notebook** or **View on GitHub** and download from there. - -## 3. Exploring the notebook using GitHub Copilot Labs - -Finally we can begin exploring the notebook following these steps: - -1. Open Visual Studio Code. -2. Ensure that the [GitHub Copilot Labs](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot-labs) extension is installed. -3. Open the previously downloaded notebook. -4. Begin exploring and analyzing the code by highlighting a desired code block and running `Explain` in the GitHub Copilot Labs extension tab. - +# Computer Vision with PyTorch + +Requirements: + +- Access to GitHub Copilot. +- GitHub Copilot Labs Extension installed. +- PyTorch. + +## Steps to replicate/test + +### 1. Installing PyTorch + +You can install PyTorch by following these steps: + +1. First, check which version of PyTorch is compatible with your system configuration and Python version by visiting the official [PyTorch](https://pytorch.org/get-started/locally/) website. +2. Once you have determined the correct version of PyTorch to install, you can use pip to install it. Open your terminal or command prompt and enter the following command: + +```bash +pip install torch torchvision +``` + +This will install the latest stable version of PyTorch and its related package torchvision. + +Alternatively, if you have a specific version of PyTorch that you want to install, you can specify the version number in the pip command. For example, to install PyTorch version 1.9.0, you can use the following command: + +```bash +pip install torch==1.9.0 torchvision==0.10.0 +``` + +3. Once the installation is complete, you can verify that PyTorch is installed correctly by opening a Python interpreter and importing the torch module: + +```python +import torch + +# Test PyTorch installation +print(torch.__version__) +``` + +If PyTorch is installed correctly, you should see the version number printed to the console. + +## 2. Download the *Transfer Learning for Computer Vision* notebook from the PyTorch Website + +Head to the [PyTorch Tutorials](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) website, and click on either **Download Notebook** or **View on GitHub** and download from there. + +## 3. Exploring the notebook using GitHub Copilot Labs + +Finally we can begin exploring the notebook following these steps: + +1. Open Visual Studio Code. +2. Ensure that the [GitHub Copilot Labs](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot-labs) extension is installed. +3. Open the previously downloaded notebook. +4. Begin exploring and analyzing the code by highlighting a desired code block and running `Explain` in the GitHub Copilot Labs extension tab. + ![GitHub Copilot Labs Extension Screen Shot](./../assets/GHL-Screenshot.jpg) \ No newline at end of file diff --git a/demos/ml-fundamentals.ipynb b/demos/ml-fundamentals.ipynb index bd336a1..3d6a7d6 100644 --- a/demos/ml-fundamentals.ipynb +++ b/demos/ml-fundamentals.ipynb @@ -1,114 +1,114 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Machine Learning Fundamentals\n", - "\n", - "By using the provided questions, we can leverage GitHub Copilot's assistance in answering (and learning) the topic at hand. Pressing `ENTER` at the end of each question, followed by pressing the `Tab` key once the CoPilot suggestion pops up, will carry out the demo as shown in the videos." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# q: What is Machine Learning?" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Supervised Learning" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# q: What is Supervised Learning?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# q: What are examples of Supervised Learning?" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training a model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# q: What is a Label and a Feature?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# q: What is a training and a test set?" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluating a model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# q: What does it mean to evaluate a model?" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.16" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Machine Learning Fundamentals\n", + "\n", + "By using the provided questions, we can leverage GitHub Copilot's assistance in answering (and learning) the topic at hand. Pressing `ENTER` at the end of each question, followed by pressing the `Tab` key once the CoPilot suggestion pops up, will carry out the demo as shown in the videos." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# q: What is Machine Learning?" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Supervised Learning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# q: What is Supervised Learning?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# q: What are examples of Supervised Learning?" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training a model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# q: What is a Label and a Feature?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# q: What is a training and a test set?" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluating a model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# q: What does it mean to evaluate a model?" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements.txt b/requirements.txt index 5584c32..30691a8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,9 +1,9 @@ -ipykernel==6.15.2 -pandas==1.4.4 -matplotlib==3.5.3 -matplotlib-inline==0.1.6 -torchvision==0.10.0 -torch==1.9.0 -numpy==1.21.2 -seaborn==0.11.2 +ipykernel==6.15.2 +pandas==1.4.4 +matplotlib==3.5.3 +matplotlib-inline==0.1.6 +torchvision==0.10.0 +torch==1.9.0 +numpy==1.21.2 +seaborn==0.11.2 scikit-learn==1.0.1 \ No newline at end of file diff --git a/test.ipynb b/test.ipynb new file mode 100644 index 0000000..e69de29