As the use of analytics proliferate, companies will need to be able to identify models that are breaking bad.
The O’Reilly Programming Podcast: A look at some of Python’s valuable, but often overlooked, features.
How to build a multilayered LSTM network to infer stock market sentiment from social conversation using TensorFlow.
Artificial intelligence, cloud technologies, and blockchain are big growth areas on O’Reilly’s learning platform.
How to decouple your Java code using a mix of dependency injection, encapsulation, and services.
Discover what design patterns are and how they can be used to communicate solutions to common problems.
From methods to tools to ethics, Ben Lorica looks at what's in store for artificial intelligence.
Learn about architectural safety measures, scaling data, caching schemes, service discovery, and more.
Experts weigh in on what we can expect from AI in 2018.
Tim O'Reilly reflects on the stories from 2017 that played out after he finished writing his new book.
How edge networks, Kubernetes, serverless and other trends will shape systems engineering and operations.
The O’Reilly Data Show Podcast: Kris Hammond on business applications of AI technologies and educating future AI specialists.
Formats, linking, and versioning are important in well-formed RESTful APIs.
Progressive web apps, offline-first development, customer experience, and other web trends to watch.
Solving problems with gradient ascent, and training an agent in Doom.
AI, blockchain, payment regionalization, and other fintech trends to watch.
How new developments in algorithms, machine learning, analytics, infrastructure, data ethics, and culture will shape data in 2018.
The O’Reilly Programming Podcast: How to effectively make the transition from monoliths to microservices.
Learn how to properly design RESTful APIs communication with clients, accounting for request structure, authentication, and caching.
Lessons from FizzBuzz for Apache MXNet.
GANs, one of the biggest breakthroughs in unsupervised learning in recent years, will bring us one step closer to general artificial intelligence.
The O’Reilly Data Show Podcast: Tim Kraska on why ML will change how we build core algorithms and data structures.
A look at why the U.S. and China are investing heavily in this new computing stack.
Reduce both experimentation time and training time for neural networks by using many GPU servers.
A glimpse behind the scenes of a high-level deep learning framework.
While open-endedness could be a force for discovering intelligence, it could also be a component of AI itself.
An overview of adoption, and suggestions to companies interested in AI technologies.
Learn how to manipulate smartphone behavior with common hyperlinks.
An overview of commercial and industrial applications of reinforcement learning.
A unified methodology for scheduling workflows, managing data, and offloading to GPUs.
The O’Reilly Programming Podcast: The impact of ARKit on developers and consumers.
Since AI's most amazing advances have been in playing games, it seems fitting that the creative challenge should involve creating games.
Common pitfalls, and an approach to narrow down the most meaningful metrics.
Decoding simple regex features to match complex text patterns.
Every line of business must have access to the digital tools needed to innovate at the edge.
A simple framework for implementing message-based, user-initiated CRUD operations.
Pascale Fung explains how emotional interaction is being integrated into machines.
Kira Radinsky describes a system that mines medical records and Wikipedia to reduce spurious correlations and provide guidance about drug repurposing.
Amr Awadallah explains the historic importance of the next wave in automation.
Watch highlights covering machine learning, smart cities, automation, and more. From Strata Data Conference in Singapore 2017.
Carme Artigas asks: Are innovations like autonomous vehicles and flying drones making our societies more intelligent?
Tony Lee outlines the unique big data and AI challenges JD.com is tackling.
Ajey Gore looks at how the impossible can be made possible with technology and data insights.
The O’Reilly Data Show Podcast: Christine Hung on using data to drive digital transformation and recommenders that increase user engagement.
A look at the rise of the deep learning library PyTorch and simultaneous advancements in recommender systems.
Thoughts on "We are the people they warned you about."
For stack scalability, elasticity at the business logic layer should be matched with elasticity at the caching layer.
Without the proper cataloging, curation, and security that self-service data platforms allow, companies are left vulnerable to cybersecurity threats and misinformation.
Melanie Johnston-Hollitt discusses a radio telescope project that will produce data on a scale that dwarfs most big data efforts.
Steve Leonard explores how Singapore is bringing together ambitious and capable people to build technology that can solve the world’s toughest challenges.
Bruno Fernandez-Ruiz discusses the tradeoffs we make to ensure safer transportation.
Felipe Hoffa says data-based conclusions are possible when stakeholders can easily analyze all relevant data.
Joshua Bloom explains why the real revolution will happen—in improved and saved lives—when machine learning automation is coupled with industrial data.
Ben Lorica explains how to guard against flaws and failures in your machine learning deployments.
Cesar Delgado joins Mick Hollison to discuss how Apple is using its big data stack and expertise to solve non-data problems.
Learn how the Defense Advanced Research Projects Agency (DARPA) has spurred significant advances in the promising field of synthetic biology.
The O’Reilly Security Podcast: The objectives of agile application security and the vital need for organizations to build functional security culture.
Using the keras TensorFlow abstraction library, the method is simple, easy to implement, and often produces surprisingly good results.
Find out how to get your voice heard and bring a positive impact to a receptive audience.
Lessons learned from building engineering teams under pressure.