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 đź’– love đź’– 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
Detect roadway lanes using Python OpenCV for project during the 5th semester at DHBW Stuttgart for lecture in digital image processing.

Find Line Detection (Image Processing) Identifying lanes of the road is very common task that human driver performs. It's important to keep the vehicl

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PyTorch code of paper "LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering"

LiVLR-VideoQA We propose a Lightweight Visual-Linguistic Reasoning framework (LiVLR) for VideoQA. The overview of LiVLR: Evaluation on MSRVTT-QA Datas

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A list of Machine Learning Art Colabs

ML Visual Art Colabs A list of cool Colabs on Machine Learning Imagemaking or other artistic purposes 3D Ken Burns Effect Ken Burns Effect by Manuel R

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Predicts an answer in yes or no.

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Ananya Gupta 1 Jan 15, 2022
Official implementation of the ICCV 2021 paper "Joint Inductive and Transductive Learning for Video Object Segmentation"

JOINT This is the official implementation of Joint Inductive and Transductive learning for Video Object Segmentation, to appear in ICCV 2021. @inproce

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[ICLR 2022] DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

DAB-DETR This is the official pytorch implementation of our ICLR 2022 paper DAB-DETR. Authors: Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi

336 Dec 25, 2022
Deep Learning: Architectures & Methods Project: Deep Learning for Audio Super-Resolution

Deep Learning: Architectures & Methods Project: Deep Learning for Audio Super-Resolution Figure: Example visualization of the method and baseline as a

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Circuit Training: An open-source framework for generating chip floor plans with distributed deep reinforcement learning

Circuit Training: An open-source framework for generating chip floor plans with distributed deep reinforcement learning. Circuit Training is an open-s

Google Research 479 Dec 25, 2022
A Free and Open Source Python Library for Multiobjective Optimization

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Project Platypus 424 Dec 18, 2022
DL & CV-based indicator toolset for the vehicle drivers via live dash-cam footage.

Vehicle Indicator Toolset Deep Learning and Computer Vision based indicator toolset for vehicle drivers using live dash-cam footages. Tracking of vehi

Alex Xu 12 Dec 28, 2021
Laplacian Score-regularized Concrete Autoencoders

Laplacian Score-regularized Concrete Autoencoders Requirements: torch = 1.9 scikit-learn = 0.24 omegaconf = 2.0.6 scipy = 1.6.0 matplotlib How to

JS 6 Dec 07, 2022
This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild with Dense 3D Representations and A Benchmark. (CVPR 2022)"

Gait3D-Benchmark This is the code for the paper "Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, Tao Mei: Gait Recognition in the Wild

82 Jan 04, 2023
The official code repo of "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection"

Hierarchical Token Semantic Audio Transformer Introduction The Code Repository for "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound

Knut(Ke) Chen 134 Jan 01, 2023
ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing

ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing ProFuzzBench is a benchmark for stateful fuzzing of network protocols. It includes a suite of

155 Jan 08, 2023
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

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Official tensorflow implementation for CVPR2020 paper “Learning to Cartoonize Using White-box Cartoon Representations”

Tensorflow implementation for CVPR2020 paper “Learning to Cartoonize Using White-box Cartoon Representations”.

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GPU-Accelerated Deep Learning Library in Python

Hebel GPU-Accelerated Deep Learning Library in Python Hebel is a library for deep learning with neural networks in Python using GPU acceleration with

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[AAAI 2022] Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding

[AAAI 2022] Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding Official Pytorch implementation of Negative Sample Matter

Multimedia Computing Group, Nanjing University 69 Dec 26, 2022
Code for the paper "SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness" (NeurIPS 2021)

SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness (NeurIPS2021) This repository contains code for the paper "Smo

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Transfer SemanticKITTI labeles into other dataset/sensor formats.

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