InfoQ Homepage Machine Learning Content on InfoQ
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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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Scaling Deep Learning to Petaflops and beyond!
Prabhat explores 2D and 3D convolutional architectures for solving pattern classification, regression and segmentation problems in high-energy physics, cosmology and climate science.
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Machine Learning Engineering - A New Yet Not So New Paradigm
Sravya Tirukkovalur discusses how ML engineering leverages skills from other engineering branches such as principles and tools, development and testing practices, and others.
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wav2letter++: Facebook's Fast Open-Source Speech Recognition System
Vitaliy Liptchinsky introduces wav2letter++, an open-source deep learning speech recognition framework, explaining its architecture and design, and comparing it to other speech recognition systems.
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Reinforcement Learning: Not Just for Robots and Games
Jibin Liu presents one of his projects at eBay where the team used RL to improve crawling of targeted web pages, starting from the basics of RL, then to why and how to use it to power web crawling.
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Petastorm: A Light-Weight Approach to Building ML Pipelines
Yevgeni Litvin describes how Petastorm facilitates tighter integration between Big Data and Deep Learning worlds, simplifies data management and data pipelines, and speeds up model experimentation.
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Applying Deep Learning to Airbnb Search
Malay Haldar discusses the work done in applying neural networks at Airbnb to improve the search beyond the results obtained with ML.
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Ludwig: A Code-Free Deep Learning Toolbox
Piero Molino introduces Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code.
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Deep Learning on Microcontrollers
Pete Warden discusses why Deep Learning is a great fit for tiny, cheap devices, what can be built with it, and how to get started.
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Federated Learning: Rewards & Challenges of Distributed Private ML
Eric Tramel discusses the basic concepts underlying the federated ML approach, the advantages it brings, as well as the challenges associated with constructing federated solutions.
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