InfoQ Homepage Machine Learning Content on InfoQ
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Predicting Cryptocurrency Exchange Rates with Stream Processing, Social Data and Online Learning
Tim Frey discusses how iunera used social data from Twitter in machine learning to predict crypto currency exchange rates.
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Deep Learning at Scale: Distributed Training and Hyperparameter Search for Image Recognition Problems
Michael Shtelma discusses methods and libraries for training models on a dataset that does not fit into memory or maybe even on the disk using multiple GPUs or even nodes.
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Machine Learning through Streaming at Lyft
Sherin Thomas talks about the challenges of building and scaling a fully managed, self-service platform for stream processing using Flink, best practices, and common pitfalls.
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The Joy of Designing Deep Neural Networks
Bradley Arsenault shares the joy he felt the first time he designed a deep neural network, and how simple intuitions on neural networks have led to greater designs and accuracy.
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We Also Can Do It! Machine Learning in Javascript!
Eliran Eliassy shows how to create a prediction model with a web application using TensorFlow.js and other deep learning tools that can run in the browser.
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Machine Learning on Mobile and Edge Devices with TensorFlow Lite
Daniel Situnayake talks about how developers can use TensorFlow Lite to build machine learning applications that run entirely on-device.
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ML/AI Panel
The panelists discuss what makes ML different from other types of applications and why it requires special tooling.
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Swift for Tensorflow
Paige Bailey demonstrates how Swift for TensorFlow can make advanced machine learning research easier and faster.
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Breakthroughs and the Future of (Deep) Reinforcement Learning
Andreas Bühlmeier discusses the foundation of Reinforced Learning and demonstrates how it is implemented. Also, he shows how to track and understand a system’s learning progress.
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ML's Hidden Tasks: A Checklist for Developers When Building ML Systems
Jade Abbott discusses the set of unexpected things that go on the "take it to production" checklist in the case of machine learning, and what are the tools that can help.
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CI/CD for Machine Learning
Sasha Rosenbaum shows how a CI/CD pipeline for Machine Learning can greatly improve both productivity and reliability.
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Machine Learning 101
Grishma Jena gives an overview of ML and delves deep into the pipeline used - right from fetching the data, the tools and frameworks used to creating models, gaining insights and telling a story.
