Provision an Ubuntu Linux Data Science Virtual Machine; the size "Standard_DS12_v2" works well
(Note: at the start of the tutorial, credentials for pre-provisioned VMs will be handed out):
https://azuremarketplace.microsoft.com/en-us/marketplace/apps/microsoft-ads.linux-data-science-vm-ubuntu

Log in to JupyterHub by pointing your web browser to https://hostname:8000 (be sure to use https, not http, and replace "hostname" with the hostname or IP address of your virtual machine). Please disgregard warnings about certificate errors.

Open a bash terminal window in JupyterHub by clicking the New button and then clicking Terminal.

In the terminal, run these four commands:

cd ~/notebooks

git clone https://github.com/Azure/active-learning-workshop.git

cd active-learning-workshop

source startup.sh

You can now log in to RStudio Server at http://hostname:8787 (unlike JupyterHub, be sure to use http, not https).

To provision many Data Science Virtual Machines using automation, see the scripts and the README file in https://github.com/Azure/active-learning-workshop/blob/master/automation_scripts

Download assets for image labeling actvity:

1. Download release package (zip) of Visual Object Tagging Tool (VOTT)
2. Dowload images pre-labeled by Active Learning pipeline from here.

Accessed via R and Python APIs, pre-trained Deep Learning models and Transfer Learning are making custom classification with large or small amounts of labeled data easily accessible to data scientists and application developers. This tutorial walks you through creating end-to-end data science solutions in R and Python on cloud-based infrastructure and consuming them in production.

Active learning helps us address the common situation where we have large amounts of data, but labeling this data is expensive. By using a preliminary model to select the cases that are likely to be most useful for improving the model, and iterating through several cycles of model training and case selection, we can often build a model using a much smaller training set (thus requiring less labeling effort) than we would otherwise need. Companies like Figure Eight (formerly CrowdFlower) and services like the Azure Custom Vision service and LUIS make use of active learning.

We have two hands-on, end-to-end active learning-based classification examples:

1. Text Classification: flagging personal attacks for moderating public discussions.
2. Image Classification: identifying different types of wood knots in lumber.

Both examples will use similar active learning approaches, but different aspects will be emphasized in the two parts:

• data exploration
• featurization
• classification
• iterative model building and active learning from selected cases
• deployment and consumption of scoring services
• scaling with distributed computing

1. Welcome and Virtual Machine Setup
2. Use Case #1: Active Learning for text classification
   2.1 Text featurization using Deep Learning
   2.2 Active Learning by Uncertainty Sampling
   2.3 Active Learning for text classification with R and Python
   2.4 Hyperparameter tuning using mmlspark
   2.5 Serving model using mmlspark
   2.6 How Uncertainty Sampling fails
3. Use Case #2: Building a custom image classifier for wood knots
   3.1 Active Learning for object detection
   3.2 Featurizing images at scale for building custom image classifier
   3.3 Active learning for image classification with R

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.