Author: Learn Azure Synapse Data Explorer
Disciplines: Analytics, Machine learning, big data, end-to-end cloud analytics.
A guide to building real-time analytics solutions to unlock log and telemetry data.
This book covers the following exciting features:
Integrate Data Explorer pools with all other Azure Synapse services
Create Data Explorer pools with Azure Synapse Studio and Azure Portal
Ingest, analyze, and serve data to users using Azure Synapse pipelines
Integrate Power BI and visualize data with Synapse Studio
Configure Azure Machine Learning integration in Azure Synapse
Manage cost and troubleshoot Data Explorer pools in Synapse Analytics
Secure Synapse workspaces and grant access to Data Explorer pools
Code samples: GitHub
My Kind of Music
Disciplines: Machine learning, sentiment analysis, text mining, search engines and text retrieval
A recommendation system that uses text mining to suggest songs to a user based on their desired mood and a few keywords. It asks the user to provide what their desired mood is from a 5-level ordinal scale (very sad, sad, neutral, happy, and very happy) and some key words, and the software recommends songs that match that user-defined sentiment and keywords.
Source code and documentation: GitHub
Guitar chord recognition
Disciplines: Machine learning, computer vision, computational photography
An application that recognizes guitar chords in real time using computer vision.
Source code and documentation: GitHub
A study of housing market trends in Austin, Texas
Disciplines: Statistical analysis (ANOVA, collinearity, multiple linear regression, others), machine learning, data cleaning
By using historical data, we attempted to predict home prices using multiple linear regression and other methods to find the best-possible prediction model for home prices in Austin. The use of detailed historical data about property sales, the intent is to offer home buyers guidance to help them understand if the sale price for a house is within overall market expectation, helping them on the decision-making process.
Source code and documentation: GitHub
Narrative visualization: house prices in Austin, Texas
Disciplines: Computer graphics, data visualization
Plotting data on web pages using D3.js and custom data.
Source code and documentation: GitHub
Video stitching and processing
Disciplines: Computational photography
Manually stitching hundreds of photos together to create a panorama. Creating a video that projects frames onto a reference plane
Documentation: GitHub
Source code: can't be shared to maintain academic integrity. Please contact me if you'd like to learn more.
Hybrid images
Disciplines: Computational photography
Hybrid images are static images that change in interpretation as a function of the viewing distance. The basic idea is that high frequency tends to dominate perception when it is available, but, at a distance, only the low frequency (smooth) part of the signal can be seen. By blending the high frequency portion of one image with the low-frequency portion of another, you get a hybrid image that leads to different interpretations at different distances. This is an implementation of the techniques described in the SIGGRAPH 2006 paper by Oliva, Torralba, and Schyns
Documentation: GitHub
Source code: can't be shared to maintain academic integrity. Please contact me if you'd like to learn more.
Image quilting
Disciplines: Computational photography
Implementation of the image quilting algorithm for texture synthesis and transfer, described in this SIGGRAPH 2001 paper by Efros and Freeman. Texture synthesis is the creation of a larger texture image from a small sample. Texture transfer is giving an object the appearance of having the same texture as a sample while preserving its basic shape.
Documentation: GitHub
Source code: can't be shared to maintain academic integrity. Please contact me if you'd like to learn more.
Gradient domain fusion
Disciplines: Computational photography
Seamlessly blend an object or texture from a source image into a target image.
Documentation: GitHub
Source code: can't be shared to maintain academic integrity. Please contact me if you'd like to learn more.
Image-based lighting
Disciplines: Computational photography
Create HDR images from sequences of low dynamic range (LDR) images and compositing 3D models seamlessly into photographs using image-based lighting techniques.
Documentation: GitHub
Source code: can't be shared to maintain academic integrity. Please contact me if you'd like to learn more.