- What a Deep Neural Net Thinks of Your Selfie — really easy to understand explanation of covolutional neural nets (the tech behind image recognition). No CS required.
- Medium’s Stack — interesting use of Protocol Buffers: We help our people work with data by treating the schemas as the spec, rigorously documenting messages and fields and publishing generated documentation from the .proto files.
- Bacterial Materials (Wired UK) — Showing a prototype worn by dancers, Yao demonstrated how bacteria-powered clothing can respond to the body’s needs. She has, in effect, created living clothes, ones that react in real time to heat and sweat mapping with tiny vents that would curl open or flatten closed as exertion levels demanded.
- Robots to the Rescue (NSF) — one 20-minute drone flight generated upwards of 800 photographs, each of which took at least one minute to inspect. This article is five lessons learned in the field of disaster robotics, and they’re all doozies.
"machine learning" entries
Learn how to deploy machine learning solutions using Azure ML.
Cloud-based machine learning platforms, like Microsoft’s Azure Machine Learning (Azure ML), provide a simplified path to create and deploy analytic solutions. Azure ML is a fully managed and secure machine learning platform that resides within the Microsoft Cortana Analytics Suite.
Azure ML workflows (known as “experiments”) are constructed using a combination of drag-and-drop modules, SQL, R, and Python scripts. The wide range of built modules support the typical steps in a machine learning workflow, from data ingestion and data munging to model construction and cross validation.
Once your Azure ML experiment is ready, there are several options to deploy it. Azure ML experiments can access large-scale data stored in Azure Blob storage, Azure SQL and Hive, to name a few options. Similarly, your experiment can write results back to multiple scalable Azure storage options.
The O'Reilly Radar Podcast: Rajiv Maheswaran on the science of moving dots, and Claudia Perlich on big data in advertising.
Subscribe to the O’Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come.
In this week’s Radar Podcast episode, O’Reilly’s Mac Slocum chats with Rajiv Maheswaran, CEO of Second Spectrum. Maheswaran talks about machine learning applications in sports, the importance of context in measuring stats, and the future of real-time, in-game analytics.
Here are some highlights from their chat:
There’s a lot of parts of the game of basketball — pick and rolls, dribble hand-offs — that coaches really care about, about analyzing how it works on offense, how to guard them. Before big data and machine learning, people basically watched the games and marked them. It turns out that people are pretty bad at marking them accurately, and they also miss a ton of stuff. Right now, machine learning tells coaches, ‘This is how many pick and rolls these two players have had over the course of the season, how often they do all the different variations, what they’re good at, what they’re bad at.’ Coaches can really find tendencies that can help them play offense, play defense, far more efficiently, based off of machine learning.
What we’re doing is having the machine match human intuition. If I’m watching a game, I know that the shot is harder if I’m farther away, if I have multiple defenders, if they’re close, if they’re closing in on me, if I’m dribbling, the type of shot I’m taking. As a human, I watch this and I have an intuition about it. Now, by giving all that data to the machine, it can make a predictor that actually matches our intuition, and goes beyond it because it can put a number onto what our intuition tells us.
A beginner's guide to evaluating your machine learning models.
Everything today is being quantified, measured, and tracked — everything is generating data, and data is powerful. Businesses are using data in a variety of ways to improve customer satisfaction. For instance, data scientists are building machine learning models to generate intelligent recommendations to users so that they spend more time on a site. Analysts can use churn analysis to predict which customers are the best targets for the next promotional campaign. The possibilities are endless.
However, there are challenges in the machine learning pipeline. Typically, you build a machine learning model on top of your data. You collect more data. You build another model. But how do you know when to stop?
When is your smart model smart enough?
Evaluation is a key step when building intelligent business applications with machine learning. It is not a one-time task, but must be integrated with the whole pipeline of developing and productionizing machine learning-enabled applications.
In a new free O’Reilly report Evaluating Machine Learning Models: A Beginner’s Guide to Key Concepts and Pitfalls, we cut through the technical jargon of machine learning, and elucidate, in simple language, the processes of evaluating machine learning models. Read more…