InfoQ Homepage Deep Learning Content on InfoQ
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Deep Learning @Google Scale: Smart Reply in Inbox
Anjuli Kannan describes the algorithmic, scaling, deployment considerations involved in a an application of cutting-edge deep learning in a user-facing product: the Smart Reply feature of Google Inbox
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Semi-Supervised Deep Learning on Large Scale Climate Models
Prabhat presents NERSc’s results in applying Deep Learning for supervised and semi-supervised learning of extreme weather patterns, scaling Deep Learning to 9000 KNL nodes on a supercomputer.
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Deep Learning for Image Understanding at Scale
Stacey Svetlichnaya discusses strategies and challenges building deep learning systems for object recognition at scale, using automatic labels in Flickr image search as a case study.
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In Depth TensorFlow
Illia Polosukhin keynotes on TensorFlow, introducing it and presenting the components and concepts it is built upon.
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Comparing Deep Learning Frameworks
Jeffrey Shomaker covers the different types of deep learning frameworks and then focuses on neural networks, including business uses and 4 of the main systems (eg. Tensor Flow) that are open sourced.
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Deep Learning Applications in Business
Diego Klabjan discusses models, implementations, and challenges developing applications for trading, forecasting, and healthcare, detailing relevant models and issues adopting and deploying them.
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Products and Prototypes with Keras
Micha Gorelick shows how to build a working product with Keras, a high-level deep learning framework, discussing design decisions, and demonstrating how to train and deploy a model.
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Deep Learning at Scale
Scott Le Grand describes his work at NVidia, Amazon and Teza, including the DSSTNE distributed deep learning framework.
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Using NLP, Machine Learning & Deep Learning Algorithms to Extract Meaning from Text
David Talby walks through building a natural language annotations pipeline with domain-specific annotators, and using deep learning to automatically expand and update taxonomies.
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TensorFlow: A Flexible, Scalable & Portable System
Rajat Monga talks about why Google built TensorFlow, an open source software library for numerical computation using data flow graphs, and what were some of the technical challenges in building it.
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Machine Learning Exposed!
James Weaver takes a deeper dive into machine learning topics such as supervised learning, unsupervised learning, and deep learning, surveying various machine learning APIs and platforms.
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Artificial Intelligence that Plays Atari Video Games: How Did Deep Mind Do It?
Kristjan Korjus discusses deep learning, reinforcement learning and their combination called deep Q-Network.
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