Tech Resources for Academic Communities

Overview

Tech Resources for Academic Communities

The content and the code in this repo are intended for computer science instruction as a collaboration with Microsoft developer advocates and Faculty / Students under the MIT license. Please check back regularly for updated versions.

Source: https://github.com/microsoft/AcademicContent

This repo provides technical resources to help students and faculty learn about Azure and teach others. The content covers cross-platform scenarios in AI and machine learning, data science, web development, mobile app dev, internet of things, and DevOps. It also includes interesting tech talks and engaging, fun tech challenges that Microsoft leads at student hackathons and Imagine Cup.

Important: We are migrating to Microsoft Learn | If you can't find what you're looking for in this repo, check out the labs on Microsoft Learn too. Many of these labs have their own built-in Azure sandbox making it easier for faculty and students to learn without requiring an Azure Subscription.

Students can get free Azure credits to explore these resources here:

  • Azure for Students | $100 in Azure for 12 months with free tier of services - no credit card required with academic verification
  • Azure for Students Starter | use select Azure products like App Services for free - no credit card required with academic verification
  • Azure Free Account | $200 in Azure for one month with free tier of services - requires a credit card and probably the best fit for faculty evaluating Azure for course instruction unless your organization has a grant or enterprise agreement.

Your feedback is appreciated - please fork this repo and contribute!

To report any issues, please log a GitHub issue. Include the content section, module number, and title, along with any error messages and screenshots.

Learn by doing with our hands-on labs

Check out our hands-on labs that can be used on your own or in the classroom. They also make for fun, easy-to-run workshops!

Lab Categories Description
AI and Machine Learning Build bots and apps backed by AI and ML using Azure and Azure Cognitive Services.
Azure Services Deploy serverless code with Azure Functions, run Docker containers, use Azure to build Blockchain networks and more.
Big Data and Analytics Spin up Apache Spark Clusters, Use Hadoop to extract information from big datasets or use Power BI to explore and visualize data.
Deep Learning These labs build on each other to introduce tools and libraries for AI. They're labeled 200-400 level to indicate level of technical detail.
Internet-of-Things Use Azure to collect and stream IoT data securely and in real time.
Web Development Quickly create scalable web apps using Node, PHP, MySQL on easy-to-use tools like Visual Studio Code and GitHub.
Web Development for Beginners, 24 lessons A curriculum with 24 lessons, assignments and five projects to build. Covers HTML, CSS and JavaScript. Also includes Pre- and Post- Quizzes. Made with teachers in mind, or as self paced learning
Machine Learning for Beginners, 25 lessons A curriculum with 25 lessons with assignments covering classic Machine Learning primarily using Scikit-learn. Covers Regression, Classification, Clustering, NLP, Time Series Forecasting, and Reinforcement Learning, with two Applied ML lessons. Also includes 50 Pre- and Post- Quizzes. Made with teachers in mind, or as self paced learning
IoT for Beginners, 24 lessons A curriculum with 24 lessons with assignments all about the Internet of Things. The projects cover the journey of food from farm to table. This includes farming, logistics, manufacturing, retail and consumer - all popular industry areas for IoT devices. Also includes Pre- and Post- Quizzes. Made with teachers in mind, or as self paced learning

Host great events and hacks

Want to host an event at your school? We can help with the resources below!

Resource
Events and Hacks These are keynotes and hack workshops that Microsoft has produced for student events. Feel free to use. Most slides also contain suggested demos and talk tracks. There's also pre-packaged coding challenge to help students explore machine learning.
Tech Talks One-off presentations on emerging or innovative tech topics with speakers notes and demos.

Other available academic resources

We also have other great educator content to help you use Azure in the classroom.

Resource
Scripts Scripts and templates built in PowerShell or BASH to help set up your classroom environment.
Azure Guides Discover what Azure technologies apply to different teaching areas.
Course Content Learning modules to complement existing course instruction. Includes presentations, speaker notes, and hands-on labs.

Attend our Reactor Workshops

We focus on developing high-quality content for all Cloud, Data Science, Machine Learning, and AI learners. Through workshops, tech talks, and hackathons hosted around the world, come learn and apply new skills to what you're interested in!

Resource
Reactor Workshops Content for our First Party Reactor Workshops can be found here.
Reactor Locations Find out schedules, learn more about each space, and see where we are opening a Reactor near you!

Content from other sources

Resource
Azure Architecture Center Cloud architecture guides, reference architectures, and example workloads for how to put the pieces of the cloud together
Microsoft AI School Content for students, developers and data scientists to get started and dive deep into the Microsoft AI platform and deep learning.
Microsoft Learn Hundreds of free online training by world-class experts to help you build your technical skills on the latest Microsoft technologies.
Technical Community Content Workshops from the community team.
Research case studies Case studies of faculty using Azure for Research collected by Microsoft Research. Submit your own Azure research stories here too!
Microsoft Research Data Sets Data sets shared by Microsoft Research for academic use.
Machine Learning Data Sets Data sets shared by Azure Machine Learning team to help explore machine learning.
MS MARCO Microsoft MAchine Reading COmprehension Dataset generated from real Bing user queries and search results.
IoT School Resources for learning about Azure IoT solutions, platform services and industry-leading edge technologies.
Azure IoT curriculum resources Hands on labs and content for students and educators to learn and teach the Internet of Things at schools, universities, coding clubs, community colleges and bootcamps
AI Labs Experience, learn and code the latest breakthrough AI innovations by Microsoft.
Channel9 Videos for developers from people building Microsoft products and services.

Structure of the docs part of this repository

This repository is designed to build a VuePress site that is hosted using GitHub Pages.

The content of this site lives in the docs folder. The main page is constructed from the README.md in that folder, and the side bar is made of the contents of the content folder.

Building the docs

To build these docs, you will need npm installed. Once you have this installed, install VuePress:

npm install vuepress

To build the docs, use the deploy.sh script. This script will build the docs, then push them to the gh-pages branch of a given fork of this project. You pass the GitHub user/org name to the script. This way you can test the build offline, then push to the parent as part of an automated script.

deploy.sh <org>

Contributing

We 💖 love 💖 contributions. In fact, we want students, faculty, researchers and life-long learners to contribute to this repo, either by adding links to existing content, or building content. Please read the contributing guide to learn more.

Owner
Microsoft
Open source projects and samples from Microsoft
Microsoft
Code image classification of MNIST dataset using different architectures: simple linear NN, autoencoder, and highway network

Deep Learning for image classification pip install -r http://webia.lip6.fr/~baskiotisn/requirements-amal.txt Train an autoencoder python3 train_auto

Hector Kohler 0 Mar 30, 2022
Code for the tech report Toward Training at ImageNet Scale with Differential Privacy

Differentially private Imagenet training Code for the tech report Toward Training at ImageNet Scale with Differential Privacy by Alexey Kurakin, Steve

Google Research 29 Nov 03, 2022
RodoSol-ALPR Dataset

RodoSol-ALPR Dataset This dataset, called RodoSol-ALPR dataset, contains 20,000 images captured by static cameras located at pay tolls owned by the Ro

Rayson Laroca 45 Dec 15, 2022
PyTorch and Tensorflow functional model definitions

functional-zoo Model definitions and pretrained weights for PyTorch and Tensorflow PyTorch, unlike lua torch, has autograd in it's core, so using modu

Sergey Zagoruyko 590 Dec 22, 2022
Ensemble Knowledge Guided Sub-network Search and Fine-tuning for Filter Pruning

Ensemble Knowledge Guided Sub-network Search and Fine-tuning for Filter Pruning This repository is official Tensorflow implementation of paper: Ensemb

Seunghyun Lee 12 Oct 18, 2022
Blind visual quality assessment on 360° Video based on progressive learning

Blind visual quality assessment on omnidirectional or 360 video (ProVQA) Blind VQA for 360° Video via Progressively Learning from Pixels, Frames and V

5 Jan 06, 2023
Multiple-Object Tracking with Transformer

TransTrack: Multiple-Object Tracking with Transformer Introduction TransTrack: Multiple-Object Tracking with Transformer Models Training data Training

Peize Sun 537 Jan 04, 2023
A large-scale video dataset for the training and evaluation of 3D human pose estimation models

ASPset-510 (Australian Sports Pose Dataset) is a large-scale video dataset for the training and evaluation of 3D human pose estimation models. It contains 17 different amateur subjects performing 30

Aiden Nibali 25 Jun 20, 2021
Code for "Primitive Representation Learning for Scene Text Recognition" (CVPR 2021)

Primitive Representation Learning Network (PREN) This repository contains the code for our paper accepted by CVPR 2021 Primitive Representation Learni

Ruijie Yan 76 Jan 02, 2023
Official implement of Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer

Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer This repository contains the PyTorch code for Evo-ViT. This work proposes a slow-fas

YifanXu 53 Dec 05, 2022
Learning View Priors for Single-view 3D Reconstruction (CVPR 2019)

Learning View Priors for Single-view 3D Reconstruction (CVPR 2019) This is code for a paper Learning View Priors for Single-view 3D Reconstruction by

Hiroharu Kato 38 Aug 17, 2022
Synthesizing and manipulating 2048x1024 images with conditional GANs

pix2pixHD Project | Youtube | Paper Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic image-to-image translatio

NVIDIA Corporation 6k Dec 27, 2022
Efficiently computes derivatives of numpy code.

Note: Autograd is still being maintained but is no longer actively developed. The main developers (Dougal Maclaurin, David Duvenaud, Matt Johnson, and

Formerly: Harvard Intelligent Probabilistic Systems Group -- Now at Princeton 6.1k Jan 08, 2023
dyld_shared_cache processing / Single-Image loading for BinaryNinja

Dyld Shared Cache Parser Author: cynder (kat) Dyld Shared Cache Support for BinaryNinja Without any of the fuss of requiring manually loading several

cynder 76 Dec 28, 2022
Supplementary code for the paper "Meta-Solver for Neural Ordinary Differential Equations" https://arxiv.org/abs/2103.08561

Meta-Solver for Neural Ordinary Differential Equations Towards robust neural ODEs using parametrized solvers. Main idea Each Runge-Kutta (RK) solver w

Julia Gusak 25 Aug 12, 2021
Voice Conversion by CycleGAN (语音克隆/语音转换):CycleGAN-VC3

CycleGAN-VC3-PyTorch 中文说明 | English This code is a PyTorch implementation for paper: CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-spectr

Kun Ma 110 Dec 24, 2022
This repository contains the implementation of the paper: Federated Distillation of Natural Language Understanding with Confident Sinkhorns

Federated Distillation of Natural Language Understanding with Confident Sinkhorns This repository provides an alternative method for ensembled distill

Deep Cognition and Language Research (DeCLaRe) Lab 11 Nov 16, 2022
Code for the paper: Sketch Your Own GAN

Sketch Your Own GAN Project | Paper | Youtube | Slides Our method takes in one or a few hand-drawn sketches and customizes an off-the-shelf GAN to mat

677 Dec 28, 2022
Dcf-game-infrastructure-public - Contains all the components necessary to run a DC finals (attack-defense CTF) game from OOO

dcf-game-infrastructure All the components necessary to run a game of the OOO DC

Order of the Overflow 46 Sep 13, 2022
An implementation of the paper "A Neural Algorithm of Artistic Style"

A Neural Algorithm of Artistic Style implementation - Neural Style Transfer This is an implementation of the research paper "A Neural Algorithm of Art

Srijarko Roy 27 Sep 20, 2022