InfoQ Homepage Machine Learning Content on InfoQ
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Panel: First Steps with Machine Learning
The panelists discuss the first principles to follow when adding ML to a system.
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Deep Learning with Audio Signals: Prepare, Process, Design, Expect
Keunwoo Choi introduces what the audio/music research societies have discovered while playing with deep learning when it comes to audio classification and regression.
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DeepRacer and DeepLens, Machine Learning for Fun! (and Profit?)
Jeremy Edberg shares his work with DeepRacer and DeepLens, talking about some of the basics of ML used in these projects and showing a DeepRacer in action.
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Reinforcement Learning: A Gentle Introduction with a Real Application
Christian Hidber shows “how” and “why” Reinforcement Learning works, using as a practical example siphonic roof drainage.
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Policing the Capital Markets with ML
Cliff Click talks about SCORE, a solution for doing Trade Surveillance using H2O, Machine Learning, and a whole lot of domain expertise and data munging.
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On a Deep Journey towards Five Nines
Aashish Sheshadri discusses how PayPal applies Seq2Seq networks to forecasting CPU and memory metrics at scale.
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Document Digitization: Rethinking OCR with Machine Learning
Nischal Harohalli Padmanabha outlines the problems faced building DL networks for document process at omni:us, limitations, the evolution of team structures, engineering practices, and other topics.
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Comparing Machine Learning Strategies Using Scikit-Learn and TensorFlow
Oliver Zeigermann looks at different ML strategies -KNN, Decision Trees, Support Vector Machines, and Neural Networks- and visualizes how they make predictions by plotting their decision boundaries.
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Code Your Way out of a Paper Bag
Frances Buontempo discusses how to program your way out of the paper bag using machine learning techniques.
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Panel: Predictive Architectures in Practice
The panelists discuss the unique challenges of building and running data architectures for predictions, recommendations and machine learning.
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Papers in Production Lightning Talks
Papers: Towards a Solution to the Red Wedding Problem, A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, and A Machine Learning Approach to Databases Indexes.
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Debuggable Deep Learning
Mantas Matelis and Avesh Singh explain how they debugged DeepHeart, a DNN that detects cardiovascular disease from heart rate data.
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