Semi-supervised, unsupervised, and adaptive algorithms for large-scale time series
The O’Reilly Data Show Podcast: Ira Cohen on developing machine learning tools for a broad range of real-time applications.
AI & ML
Few technologies have the potential to change the nature of work and how we live as artificial intelligence (AI) and machine learning (ML).
Practical machine learning techniques for building intelligent applications
The O’Reilly Data Show Podcast: Mikio Braun on practical data science, deep neural networks, machine learning, and AI.
Keynotes from Strata + Hadoop World in San Jose 2016
Watch full keynotes covering data science, data tools, enterprise adoption and more. From Strata + Hadoop World in San Jose 2016.
Learning from Tay
Whether our prejudices are overt or hidden, our artificial intelligentsia will reflect them.
Democratizing business analytics
The O’Reilly Data Show Podcast: Duncan Ross on the evolution of analytics, data mining, and data philanthropy.
The inherent clumpiness of randomness
Finding patterns isn't really a question about random processes; it's a question about the human brain.
A poet does TensorFlow
Pete Warden’s instructions on building a deep learning classifier looked so simple, I had to try it myself.
Learning in higher dimensions
Tensor methods to solve machine learning challenges: An interview with Anima Anandkumar.
Stream processing and messaging systems for the IoT age
The O’Reilly Data Show podcast: M.C. Srivas on streaming, enterprise grade systems, the Internet of Things, and data for social good.
Using Apache Spark to predict attack vectors among billions of users and trillions of events
The O’Reilly Data Show podcast: Fang Yu on data science in security, unsupervised learning, and Apache Spark.
Metadata services can lead to performance and organizational improvements
The O’Reilly Data Show podcast: Joe Hellerstein on data wrangling, distributed systems, and metadata services.
Building a business that combines human experts and data science
The O’Reilly Data Show podcast: Eric Colson on algorithms, human computation, and building data science teams.
Is 2016 the year you let robots manage your money?
The O’Reilly Data Show podcast: Vasant Dhar on the race to build “big data machines” in financial investing.
When building an enterprise data strategy, consider “why?” before “how?”
Roadmaps should be driven by business goals, not technology.
Investing in big data technologies
The O’Reilly Data Show podcast: A fireside chat with Ben Horowitz, plus Reynold Xin on the rise of Apache Spark in China.
Building a scalable platform for streaming updates and analytics
The O’Reilly Data Show podcast: Evan Chan on the early days of Spark+Cassandra, FiloDB, and cloud computing.
The current state of machine intelligence 2.0
Autonomous systems and focused startups among major changes seen in past year.
Programming, oaths, and users
The Programmer's Oath is missing one essential element: the customer.
Graph databases are powering mission-critical applications
The O’Reilly Data Show Podcast: Emil Eifrem on popular applications of graph technologies, cloud computing, and company culture.
Machine learning in the wild
A bridge between robust control and reinforcement learning.
What to look for in a data scientist
How an algorithm is to a data scientist what a compound microscope is to a biologist.
Jai Ranganathan on architecting big data applications in the cloud
The O’Reilly Data Show podcast: The Hadoop ecosystem, the recent surge in interest in all things real time, and developments in hardware.
Building systems for massive scale data applications
The O’Reilly Data Show podcast: Tyler Akidau on the evolution of systems for bounded and unbounded data processing.
Turning big data into actionable insights
The O’Reilly Data Show podcast: Evangelos Simoudis on data mining, investing in data startups, and corporate innovation.