From deep learning to decoupling, here are the data trends to watch in the year ahead.
Best practices and scalable workflows for reproducible data science.
Putting deep learning into practice with new tools, frameworks, and future developments.
What data scientists need to know about production—and what production should expect from their data scientists.
The O’Reilly Data Show Podcast: Greg Diamos on building computer systems for deep learning and AI.
A high-level tour of modern data-processing concepts.
Results and analysis from O'Reilly's fourth annual survey of data science professionals.
Watching the appeal and applications of machine intelligence expand.
An informative, visual, and interactive MNIST tutorial.
Max Shron and Sasha Laundy explore tactics for need-finding and problem scoping that make it possible to put investments in data to profitable use.
An efficient, fast, and repeatable selection method that works on very large data sets.
Building and training your first TensorFlow graph from the ground up.
Mike Barlow examines the growth of sophisticated cloud-based AI and machine learning services for a growing market of developers and users in business and academia.
Learn how to ship, parse, store, and analyze logs.
Get intensive, hands-on training on current critical data technology topics, led by instructors from O'Reilly's unparalleled network of tech innovators and expert practitioners.
Jerry Overton walks you through how to build and execute a data strategy, how to write algorithms, and how to experiment on an enterprise scale.
Jesse Anderson will show you how to recognize the opportunities, avoid the problems, and get the most value from your data.
Oriole Online Tutorial
San Jose, CA
Make Data Work
“Data is having an impact on business models and profitability. It’s hard to find a non-trivial application that doesn’t use data in a significant manner.”— Ben Lorica, Director of Content Strategy for Data at O'Reilly Media