InfoQ Homepage Machine Learning Content on InfoQ
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AI for Software Testing with Deep Learning: Is It Possible?
Emerson Bertolo discusses lessons learned when using pre-trained Convolutional Neural Networks (CNN) models, Image Detection APIs and CNN's built from scratch for this purpose.
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Rethinking HCI with Neural Interfaces @CTRLlabsCo
Adam Berenzweig talks about brain-computer interfaces, neuromuscular interfaces, and other biosensing techniques that can eliminate the need for physical controllers.
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Using Data Effectively: beyond Art and Science
Hilary Parker talks about approaches and techniques to collect the most useful data, analyze it in a scientific way, and use it most effectively to drive actions and decisions.
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Building the Enchanted Land
Grady Booch examines what AI is and what it is not, as well as how it came to be and where it's headed. Along the way, he examines some best practices for engineering AI systems.
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What Computers Can Teach Us about Humans: Machine Learning in Marketing
Melinda Han Williams discusses using machine learning in marketing.
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Big Data and Deep Learning: A Tale of Two Systems
Zhenxiao Luo explains how Uber tackles data caching in large-scale DL, detailing Uber’s ML architecture and discussing how Uber uses Big Data, concluding by sharing AI use cases.
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Fast Log Analysis by Automatically Parsing Heterogeneous Log
Debnath & Dennis present a solution inspired by the unsupervised machine learning techniques for automatically generating RegEx rules from a set of logs with no (or minimal) human involvement.
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Teaching a Machine to Code
Samir Talwar discusses different techniques, architectures and optimizations tried in the process of teaching a machine to write code using neural networks, simulations and everything in between.
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How Machines Help Humans Root Case Issues @ Netflix
Seth Katz discusses ways to build tools designed to enhance the cognitive ability of humans through automated analysis to speed root cause detection in distributed systems.
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Engineering Systems for Real-Time Predictions @DoorDash
Raghav Ramesh presents DoorDash’s thoughts on how to structure ML systems in production to enable robust and wide-scale deployment of ML, and shares best practices in designing engineering tooling.
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Deep Learning for Application Performance Optimization
Zoran Sevarac presents his experience and best practice for autonomous, continuous application performance tuning using deep learning.
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Neural Networks across Space and Time
Dave Snowdon starts with a brief introduction to deep neural networks, why they are important and how they work. He covers 2 of the most important deep neural architectures: convolutional & recurrent.
