How OpenAI Uses Deep Learning
4 engineering articles about Deep Learning from OpenAI's engineering team
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The article discusses the advancements in AI efficiency, highlighting a significant decrease in the compute required to train neural networks since 2012.
Danny Hernandez
14 min read
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The article discusses adversarial examples in machine learning, which are inputs deliberately designed to mislead models.
Ian Goodfellow
10 min read
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The article discusses a novel approach to machine learning that enhances privacy while utilizing sensitive training data through a method called Private Aggregation of Teacher Ensembles (PATE).
Nicolas Papernot
2 min read
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The article discusses the infrastructure necessary for deep learning, emphasizing the importance of a robust setup to facilitate research and experimentation.
AWSChefDeep LearningDockerKerasKubernetesNeural NetworksOpenCVPackerTensorBoardTensorFlowTerraformWhisper
Vicki Cheung
9 min read
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