Every two weeks, we find the most interesting data science links from around the web and collect them in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to get up to speed on interesting resources.
- Are You Setting Your Data Scientists Up to Fail?
“Getting as much as they can from analytics is critical for companies seeking to monetize their data, become data-driven, and put their data to work. Yet most find this difficult. Indeed, the failure rate of analytics projects remains distressingly high.” - For artificial intelligence to thrive, it must explain itself
If it cannot, who will trust it? - Should data scientists adhere to a Hippocratic oath?
“The tech industry is having a moment of reflection. Even Mark Zuckerberg and Tim Cook are talking openly about the downsides of software and algorithms mediating our lives.” - Sky-High Salaries Are the Weapons in the AI Talent War
Want to command the crazy wages? Here’s what you need to bring. - It’s Getting Harder to Tell Banks From Tech Companies
Just imagine the day that Goldman Sachs offers an Uber-for-mergers app. - Facial Recognition Is Accurate, if You’re a White Guy
When the person in the photo is a white man, the software is right 99 percent of the time. - Interpretable Machine Learning through Teaching
From OpenAI: “We’ve designed a method that encourages AIs to teach each other with examples that also make sense to humans.” - Introduction to Learning to Trade with Reinforcement Learning
“The academic Deep Learning research community has largely stayed away from the financial markets. Maybe that’s because the finance industry has a bad reputation, the problem doesn’t seem interesting from a research perspective, or because data is difficult and expensive to obtain.” This sounds cool, but… - Deep Reinforcement Learning Doesn’t Work Yet
“Whenever someone asks me if reinforcement learning can solve their problem, I tell them it can’t. I think this is right at least 70% of the time.” - Greedy, brittle, opaque, and shallow: the downsides to deep learning
We’ve been promised a revolution in how and why nearly everything happens. But the limits of modern artificial intelligence are closer than we think. - ‘Black Mirror’ technology: Chinese police don high-tech glasses to nab suspects
Chinese police are sporting high-tech sunglasses that can spot suspects in a crowded train station, the newest use of facial recognition technology that has drawn concerns among human rights groups. - The 5 Clustering Algorithms Data Scientists Need to Know
A quick and comprehensive overview. - Introduction to GIS with R
Spatial data with the sp and sf packages. - How to write production-ready data science code?
The ability to write a production-level code is one of the sought-after skills for a data scientist role— either posted explicitly or not. For a software engineer turned data scientist this may not sound like a challenging task as they might have already perfected their skill at developing production level codes and deployed into production several times. - TensorFlow for R
“Over the past year we’ve been hard at work on creating R interfaces to TensorFlow, an open-source machine learning framework from Google. We are excited about TensorFlow for many reasons, not the least of which is its state-of-the-art infrastructure for deep learning applications.” - 8 Deep Learning Best Practices I Learned About in 2017
Great overview of the little tricks there are to learn in the version 2 edition of fast.ai’s Deep Learning course.
