InfoQ Homepage Deep Learning Content on InfoQ
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An Open Source Infrastructure for PyTorch
Mark Saroufim discusses tools and techniques to deploy PyTorch in production.
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Visual Intro to Machine Learning and Deep Learning
Jay Alammar offers a mental map of Machine Learning prediction models and how to apply them to real-world problems with many examples from existing businesses and products.
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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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Swift for Tensorflow
Paige Bailey demonstrates how Swift for TensorFlow can make advanced machine learning research easier and faster.
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ML in the Browser: Interactive Experiences with Tensorflow.js
Victor Dibia provides a friendly introduction to machine learning, covers concrete steps on how front-end developers can create their own ML models and deploy them as part of web applications.
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Peloton - Uber's Webscale Unified Scheduler on Mesos & Kubernetes
Mayank Bansal and Apoorva Jindal present Peloton, a Unified Resource Scheduler for collocating heterogeneous workloads in shared Mesos clusters.
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Automating Software Development with Deep Learning
Emil Wallner discusses the state of the art in software development automation, its current weaknesses, and areas that are ready for production.
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Evoking Magic Realism with Augmented Reality Technology
Diana Hu explores how building a real world system is more a software engineering art, requiring making choices among a set of tradeoffs.
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Getting Started in Deep Learning with TensorFlow 2.0
Brad Miro explains what deep learning is, why one may want to use it over traditional ML methods, as well as how to get started building deep learning models using TensorFlow 2.0.
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Deep Learning for Recommender Systems
Oliver Gindele discusses how some DL models can be implemented in TensorFlow, starting from a collaborative filtering approach and extending that to more complex deep recommender systems.
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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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