Issue 143
— In the News —
The Human Insights Missing from Big Data
Why is it that so many organizations make bad decisions, in spite of having access to enormous amounts of data? In this Ted Talk, Tricia Wang identifies the missing piece that she calls, "Thick Data." It's difficult to quantify but is vital for making Big Data work.
Artificial Intelligence Is Stuck. Here’s How to Move It Forward.
There's a lot of hype around AI these days but even so, it has a long way to go. Gary Marcus, an expert in human learning, has an idea to speed things along.
Your 'Anonymous' Browsing Data Isn't Actually Anonymous
It turns out that it doesn't take much to identify specific users in anonymous browsing data. Using data that was easily obtained by online brokers, researchers were able to identify people and discover their entire month-long browsing history from an anonymous dataset. "These companies have lost their minds when it comes to data collection."
How data can help us understand cancer and its treatment
The dream, says a bioengineer, is to have cancer-relevant medical data flow unimpeded around the world, so that everyone, wherever they are, can see and use this information. This is a great idea and would probably be super useful for advancing health care. But one of the problems, of course, is the idea that people will freely share health-related data in a time when pre-existing conditions might be used against them. Even "anonymizing" data wouldn't guarantee privacy. There are important goals here. And important issues that need to be figured out first.
— Sponsored Link —
Learn R, Python and SQL for Data Science with DataCamp
Complete interactive coding challenges and watch videos right in your browser for accelerated and efficient learning. Over 120 hours of content from the best instructors in academia and the data science industry. Topics include R and Python programming, SQL, Spark, data visualization, statistics, data manipulation, machine learning, and applied finance. Start today!
— Tools and Techniques —
The AI Hierarchy of Needs
In spite of all the excitement, most organizations are not ready for AI. In this post, Monica Rogati explains why that is and offers a super clear path for creating the right foundation.
An end to end implementation of a Machine Learning pipeline
This tutorial shows how to design a complete machine learning pipeline, including the implementation decisions that you need to make if you want to do real work. There's code here but this isn't a copy/paste tutorial. It's intended to get you thinking.
Detecting bats by recognising their sound with Tensorflow
This step by step tutorial shows how to build a sound recognition pipeline for detecting bats. Covers sound clip management, analysis, and a simple neural network. This isn't complicated and the ideas here could be easily extended.
Cutting Edge Deep Learning for Coders
fast.ai just released the second part of their popular deep learning course. This part covers a variety of cutting-edge topics including neural translation, generative models, memory networks, and applications for structured data and time series. This is a free online course and the pre-requisites for the series are fairly modest.
— Resources —
Essential AI Cheatsheets
Essential Cheat Sheets for deep learning and machine learning. Includes cheatsheets for Tensorflow, Keras, Numpy, Scipy, Pandas, Scikit-learn, Matplotlib, etc, etc.
— In Case You Missed It —
Be sure to catch the most popular links from last week's issue...
— About —
Data Elixir is curated and maintained by @lonriesberg. If some awesome person forwarded this issue to you, subscribe for free at dataelixir.com and get it delivered every week.
Sign up for Free
and join the thousands of data lovers who already start their week with us.
No spam, ever. We'll never share your email address and you can opt out at any time.
No spam, ever.