InfoQ Homepage Programming Content on InfoQ
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AI from an Investment Perspective
The panelists discuss AI from an investment perspective, the challenges, the risks, trends, the role of Deep Learning, successful AI use cases, and more.
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Machine Learning at Scale
Aditya Kalro discusses using large-scale data for Machine Learning (ML) research and some of the tools Facebook uses to manage the entire process of training, testing, and deploying ML models.
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Straggler Free Data Processing in Cloud Dataflow
Eugene Kirpichov describes the theory and practice behind Cloud Dataflow's approach to straggler elimination, and the associated non-obvious challenges, benefits, and implications of the technique.
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Extreme Programming Meets Real-time Data
Tom Johnson and Gel Goldsby talk about scaling problems they encountered at Unruly, and where extreme programming values led them.
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AI in Medicine
Anthony Chang presents the current status of AI in medicine and the foreseeable future in front of it.
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Goodbye PrintGCDetails... and Other JDK 9 Changes!
Tony Printezis talks about the major changes and improvements coming in JDK 9 that will affect (but also help) anyone who's interested in Java performance monitoring, profiling, and tuning.
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Products and Prototypes with Keras
Micha Gorelick shows how to build a working product with Keras, a high-level deep learning framework, discussing design decisions, and demonstrating how to train and deploy a model.
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Our Concurrent Past; Our Distributed Future
Joe Duffy talks about the concurrency's explosion onto the mainstream over the past 15 years and attempts to predict what lies ahead for distributed programming, from now til 15 years into the future.
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Deep Learning at Scale
Scott Le Grand describes his work at NVidia, Amazon and Teza, including the DSSTNE distributed deep learning framework.
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Reactive & Asynchronous - Adventures with APIs in Financial Trading
Michael Barker discusses several low-latency APIs used for financial trading, what makes them fast and how they compare to HTTP/REST/JSON/XML APIs.
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Using NLP, Machine Learning & Deep Learning Algorithms to Extract Meaning from Text
David Talby walks through building a natural language annotations pipeline with domain-specific annotators, and using deep learning to automatically expand and update taxonomies.
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Engineering You
Lynn Langit and Martin Thompson explore the individual practices and techniques that can help bring out the engineer in us.
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