Fortunately, this should improve the productivity and focus of our community, rather than harm it. Our biggest successes are still ahead
— François Chollet (@fchollet) June 16, 2018
Fortunately, this should improve the productivity and focus of our community, rather than harm it. Our biggest successes are still ahead
— François Chollet (@fchollet) June 16, 2018
We haven't yet solved even 10% of the problems we could solve with existing AI/ML techniques. Even if new research were to deliver nothing from now on, there still wouldn't be another AI winter. AI/ML will keep on delivering for years to come.
— François Chollet (@fchollet) June 16, 2018
“We find that a graphical correction decreases misperceptions... more than an equivalent text correction" Very interesting article & study: https://t.co/SzZPGMC56M via @jdschramm #dataviz
— Cole Knaflic (@storywithdata) June 16, 2018
When I first began learning to code, I saw `public static void main` in Java and was super confused. I was also reading a book that spent a page explaining each keyword. JavaScript was a relief coming from that verbosity.
— Pranay Prakash (@pranaygp) June 15, 2018
Well, today I wrote this line of code 🤷♀️ pic.twitter.com/6kzXcsYWRu
Something you develop the longer you do data analysis is a "spidey sense" when something "looks wrong" that can often lead to uncovering a problem with your dataset or analysis. pic.twitter.com/7EwcXiVoyu
— Data Science Renee (@BecomingDataSci) June 15, 2018
—Deliberately design end-to-end workflows.
— 👩💻 DynamicWebPaige @ $HOME 🏞 (@DynamicWebPaige) June 15, 2018
—Reduce cognitive load for your users.
—Provide helpful feedback to your users.
In the long run, good design always wins, because it makes its adepts more productive and impactful.
Good design is infectious. ✨https://t.co/KdG7IT7Kwh pic.twitter.com/bXKkQpqJLP
#DataScience has taught me to always aspire to be merely average.
— Nihilist Data Scientist (@nihilist_ds) June 15, 2018
That way I will consistently match everyone’s expectations.#statistics
Yes! Machine learning product design boils down to:
— Drew Breunig (@dbreunig) June 15, 2018
1. Having unique data
2. Asking the right question for your users
3. Figuring how to get more interesting data to ask better questions https://t.co/dqVpKzbWlw
Twitter’s ML platform is now based on #TensorFlow. As usual, it came down to deployment. https://t.co/oLjG26N2MT pic.twitter.com/K7UrU6Em5C
— Delip Rao (@deliprao) June 14, 2018
Questions to ask about software using machine learning https://t.co/dYnGt6OihT pic.twitter.com/0lMbweWXLQ
— Rachel Thomas (@math_rachel) June 14, 2018
Now that @chrmanning has called me out, I will take my duty seriously and tweet more to stave off the impending AI Winter. 😛 https://t.co/A7R0d2REPD
— Andrew Ng (@AndrewYNg) June 14, 2018
Feature engineering is often the way to most improve the performance of your ML system. Domain experts make the best feature engineers. https://t.co/QqsyOL7vUO
— Brandon Rohrer (@_brohrer_) June 14, 2018