Merging the gaps between data science and engineering, and what each side can learn from the other.
A closer look at the reasoning inside your deep networks.
The O’Reilly Data Show Podcast: Reza Zadeh on deep learning, hardware/software interfaces, and why computer vision is so exciting.
Kurt Brown discusses services in use, such as Genie, Metacat, Charlotte, and Microbots.
Tools, trends, what pays (and what doesn’t) for data professionals in Europe
Watching the appeal and applications of machine intelligence expand.
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The adventures in deep learning and cheap hardware continue!
June Andrews talks about simple, cost-effective algorithmic computing at scale.
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.
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.
Adam Breindel shows you the modern best practices for Spark 2.1, using the latest Spark features, for high-performance analytics, processing, and modeling on large-scale data sets.
Katharine Jarmul will show you how to use Python libraries to speed up the data wrangling process and automate data cleaning, how to handle messy data, and how to write data unit tests that monitor data validity.
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