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Set up repo for intro to cv series
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‎2023/SP23/intro-cv-series/.acm-ai-metadata.yml‎

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title: "Intro to CV Workshop Series"
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description: "This workshop series will teach you the fundamentals of machine leraning and computer vision. We will cover neural networks, CNNs, and more."

‎2023/SP23/intro-cv-series/Default_Main_Header_Dark.png‎

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‎2023/SP23/intro-cv-series/Default_Main_Header_Light.png‎

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‎2023/SP23/intro-cv-series/README.md‎

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<!--
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If you have any questions about this template, feel free to ask
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your Director for help!
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-->
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<!--
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SECTION: Header
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---------
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Request new headers from you Director to fit your workshop!
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-->
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![Alt Text (Intro to CV Workshop Series)](./Default_Main_Header_Light.png#gh-light-mode-only)
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![Alt Text (Intro to CV Workshop Series)](./Default_Main_Header_Dark.png#gh-dark-mode-only)
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The official ACM AI **Intro to Computer Vision Workshop Series** repository.
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<!--
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SECTION: Table of Contents
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---------
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Mandatory Sections:
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- File Directory Structure
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- Getting Started
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- Give an interesting description of your workshop!
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- E.g. you can use the marketing descriptiong (w/o the emojis
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and make the nouns general ('you' becomes 'the reader'))
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- Resources
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- Images, papers, etc
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Other Possible Sections:
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- Datasets
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- Separate from 'Resources' to distinguish between inspiration and
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things like datasets used in a notebook
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- Anything else you'd like, but try not to be redundant!
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-->
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<!--
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SECTION: File Directory Structure
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---------
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Write out your File Directory Structure below (make sure it's up-to-date)
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-->
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# 1. File Directory Structure
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```bash
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intro-cv-series
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| -- workshop-1
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| -- [Workshop 1 Resources]
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| -- ...
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| -- Default_Main_Header_Dark.png
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| -- Default_Main_Header_Light.png
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| -- ...
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| -- README.md
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```
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<!--
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SECTION: Getting Started
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---------
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Brief description of your workshop here
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-->
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# 2. Getting Started
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## 2.1 Workshop 1 - Intro to CV: Pytorch and Neural Networks
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<!--
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You can write something up for each worksop or use their marketing descriptions.
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-->
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An introduction to Pytorch, basic machine learning formulations, and neural networks. This workshop will lead students through developing a multi layer neural network to classify handwritten digits from the MNIST dataset, and provide the foundation and intuitions for students to begin tackling basic computer vision problems on their own, and empower their own learning.
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Slides:
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<!--
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SECTION: Resources
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---------
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Make sure to cite everything you use, whether directly or for inspiration!
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-->
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## 2.2 Workshop 2 - Intro to CV: Convolutional Neural Networks
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<!--
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You can write something up for each worksop or use their marketing descriptions.
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-->
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N/A
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Slides:
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<!--
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SECTION: Resources
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---------
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Make sure to cite everything you use, whether directly or for inspiration!
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-->
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## 2.3 Workshop 3 - Intro to CV
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<!--
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You can write something up for each worksop or use their marketing descriptions.
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-->
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N/A
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Slides:
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<!--
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SECTION: Resources
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---------
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Make sure to cite everything you use, whether directly or for inspiration!
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-->
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# 3. Resources
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## 3.1 Workshop 1 - Intro to CV: Pytorch and Neural Networks
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-
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## 3.2 Workshop 2 - Intro to CV: Convolutional Neural Networks
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## 3.3 Workshop 3 - Intro to CV
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<!--
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SECTION: Author Info
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---------
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Make sure to give yourself credit for your work by listing yourself and
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your partners below! Add your LinkedIn and GitHub!
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-->
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# 4. Author Info
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- Alan Li: [LinkedIn](https://www.linkedin.com/in/alan-li-2001/) | [GitHub](https://github.com/yuyeon)
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- Stefanie Dao: [LinkedIn](https://www.linkedin.com/in/stefanie-dao/) | [GitHub](https://github.com/StefanieDao)
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- Weiji
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- Arth Shukla: [Site](https://arth.website/) | [LinkedIn](https://www.linkedin.com/in/arth-shukla/) | [GitHub](https://github.com/arth-shukla)

‎2023/SP23/intro-cv-series/workshop-1/Intro_to_CV.ipynb‎

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# Intro to CV Workshop 1: Getting Started with Computer Vision"
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],
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"metadata": {
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"id": "lrPpS1rpCK-h"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Section 1 - Overview of Computer Vision\n"
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],
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"metadata": {
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"id": "X_yXZfg-9Fa-"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### 1.1 Introduction of Computer Vision\n"
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],
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"metadata": {
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"id": "t-rte0n6Jrn5"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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" ### 1.2 The MNIST Dataset"
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],
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"metadata": {
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"id": "LAy-avyOKTPw"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Section 2 - Basics of basics"
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],
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"metadata": {
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"id": "TknP7gWmCJvB"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### 2.1 Pytorch Basics"
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],
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"metadata": {
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"id": "xClsDXlOJNZM"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### 2.2 Model Training and Evaluation"
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],
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"metadata": {
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"id": "vKDff4XKJgx_"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Section 3 - Formulating a Machine Learning problem "
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],
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"metadata": {
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"id": "pcibKmYR_OTW"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"(feel free to edit the outline)"
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],
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"metadata": {
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"id": "tIpm_ep-K3lQ"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### 3.1 Set up MLP Architecture (NN layers, activation function)\n"
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],
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"metadata": {
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"id": "hanQrmfxCH5C"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### 3.2 Loss function and Regularization (L1/L2/Dropout)"
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],
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"metadata": {
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"id": "k2zSw1jLKvM9"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### 3.3 Learning and Evaluation (Gradient Descent, SGD)\n"
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],
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"metadata": {
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"id": "ZAqFgipcKx6S"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Section 4 - Making a MLP piece-by-piece"
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],
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"metadata": {
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"id": "7gJNzxuBLDyv"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Section 5 - Putting it together for CV (Can change this later)"
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],
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"metadata": {
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"id": "tbIiaUguLUoU"
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}
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}
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]
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}

‎2023/SP23/intro-cv-series/workshop-1/README.md‎

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<!--
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If you have any questions about this template, feel free to ask
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your Director for help!
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-->
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<!--
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SECTION: Header
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---------
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Request new headers from you Director to fit your workshop!
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-->
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![Intro to CV: Search Algorithms](./figures/W1_Header_Light.png#gh-light-mode-only)
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![Intro to CV: Search Algorithms](./figures/W1_Header_Dark.png#gh-dark-mode-only)
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The official ACM AI **Intro to CV: Pytorch and Neural Networks** repository. Here you can find the slides and notebook for the workshop. Feel free to go through it yourself to learn how to train a neural network for classifying handwritten digits!
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<!--
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SECTION: Table of Contents
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---------
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Mandatory Sections:
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- File Directory Structure
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- Workshop Recording
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- if you recorded your workshop, please make it available here
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- Getting Started
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- Give an interesting description of your workshop!
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- E.g. you can use the marketing descriptiong (w/o the emojis
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and make the nouns general ('you' becomes 'the reader'))
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- Resources
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- Images, papers, etc
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Do NOT Include:
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- Author Info
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- This should only be in the main README for your series
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Other Possible Sections:
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- Anything else you'd like, but try not to be redundant!
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- Make sure it's not already in the main series README or
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another section
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-->
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<!--
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SECTION: Workshop Video
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---------
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Most, if not all, workshops should have recordings. Once the recording
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is posted to the ACMUCSD YT channel (https://www.youtube.com/channel/UCyjPATFqc3FwOiuqJ2UG1Eg), replace the text with an <img> element.
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-->
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# 1. Getting Started
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<!--
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You can write something up or use the marketing description.
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-->
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## 1.1 Environment Setup
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```
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conda env create -f environment.yaml
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conda activate ai
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```
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Workshop "Intro to CV: Neural Networks" consists of 2 components:
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- [Notebook](<!-- Local Path to Notebook -->) with completed code and explanations.
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- [Slide Deck](<!-- Local Path to Slide Deck -->) for the presentation slides
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<!--
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Note: The above list will depend on your specific workshop.
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-->
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# 2. Workshop Video
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*Will be added as soon as uploaded to YouTube*
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<!--
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<div align="center">
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<a href="YT Video Link">
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<img
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src="YT Max Res Thumbnail Link"
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alt="Screen reader-compatible alt text"
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width="500px"
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/>
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</a>
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</div>
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-->
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<!--
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SECTION: File Directory Structure
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---------
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Write out your File Directory Structure below (make sure it's up-to-date)
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-->
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# 3. File Directory Structure
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```bash
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intro-ai-series
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| -- figures
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| -- DefaultW1_Header_Dark.png
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| -- DefaultW1_Header_Light.png
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| -- src
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| -- videos # where the videos of our agent traversing the FrozenLake grid are stored
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| -- main.py # calls the core search algorithms
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| -- search.py # where we define the core search algorithms for our AI agent
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| -- test.py # for testing determinstic trajectories in FrozenLake enviroment
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| -- README.md
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```
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<!--
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SECTION: Getting Started
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---------
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Brief description of your workshop here
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-->

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