Issue 261
— Insight —
Local-first software: you own your data, in spite of the cloud
Cloud apps are great for accessing data from any of your devices and for enabling real-time collaboration. But who owns the data? And what happens when organizations shut down and apps stop working? Here's a proposal for a new approach and there's a lot to think about. Follow this post by Adrian Colyer and start down the rabbit hole.
Global AI Survey: AI proves its worth, but few scale impact
Most companies report measurable benefits from AI where it has been deployed; however, much work remains to scale impact, manage risks, and retrain the workforce. This new report from McKinsey & Company shows how a group of high performers is leading the way.
Great post by Laurie Shaw that summarizes her work on team formation analysis and shows how her new approach of measuring and classifying team formations as a function of game state is more effective than other techniques. This was recently presented at the FC Barcelona Sports Analytics Summit and won the "best research paper prize." This tutorial by Peter Ellis shows how to perform cost-benefit analysis in R and how to build in uncertainty in a much clearer way than is generally done. In this tutorial, Gideon Mendels shows to analyze, explore and understand audio data using Comet’s meta machine-learning platform. This is easy to follow, starting with a introduction to audio data and digital signal processing. As business leaders increase investment in advanced analytics, data science, and AI, many struggle to assess the return on those efforts. This is due to the inaccurate measurement and reporting of success. Fortunately, we can do better. This webinar, with Q&A, will give leaders the tools to identify and assess the possible impact of data science projects. // sponsored
— Tools and Techniques —
Using Data to Analyse Team Formations ⚽
Cost-benefit analysis in R
How to apply machine learning and deep learning methods to audio analysis
Metis Webinar | AI ROI: The Questions You Need to Be Asking
Nice collection of interactive notebooks about quantitative finance. There's also a worthwhile discussion about this project and related resources on Hacker News. This collection of excerpts from the upcoming book, Deep Learning with PyTorch, is a nice introduction to building and training neural networks.
— Resources —
Financial Models Numerical Methods
Deep Learning with PyTorch
Camera traps are an invaluable tool in conservation research, but the sheer amount of data they generate presents a huge barrier to using them effectively. In this new competition from DrivenData, you can help conservation research by building the best algorithms for wildlife detection and compete for a share of 20K USD in prizes.
— Challenges —
Hakuna Ma-data: Identify Wildlife on the Serengeti
Easy to follow, step by step tutorial that shows how to create streetmaps for any city using osmdata and ggplot2. Technical ability is important but if you lack grounding in fields such as psychology, stats, and communication, your visualizations won't go very far. In this post, Michael Correll summarizes key talks from VIS2019 that address non-technical challenges of communicating with data.
— Data Viz —
Streetmaps
A Reflection on VIS2019: Or, How Doomed Are We?
This new section of Chip Huyen's upcoming Machine Learning Interviews book offers interview strategies, linked references, case studies and exercises for thinking through machine learning system design. This is a great read for anyone who's either hiring or is interested in a career that involves production ML.
— Career —
Machine Learning Systems Design
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