Collecting Data from Human Feedback for Generative AI
Human feedback is essential to the accuracy and continual improvement of generative AI systems. Incorporating human ranking of alternative model outputs into model reward functions is a key feature of the training
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Making dataset management easier for all stakeholders
In a previous post, we showed how Oxen's Remote Workspaces radically
Machine Inference != Machine Learning
One of the reasons I love the AI community is the openness to share research and build on top of
We version our code, why not our data?
All machine learning solutions start with a good dataset. The author of โDeep Learning with Pythonโ goes as far as stating Spending more effort and money on data collection almost always yields a much greater return
๐ Contribute to Massive Datasets in Seconds with Oxen.aiโs Remote Workspaces
The datasets used for training and benchmarking machine learning models are incredibly large and continue to grow rapidly. ImageNet, a
Generative Deep Learning Book - Chapter 5 - Autoregressive Models
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Generative Deep Learning Book - Chapter 4 - Generative Adversarial Networks (GANs)
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Generative Deep Learning Book - Chapter 3 - Variational Auto Encoders
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Generative Deep Learning Book - Chapters 1 & 2 - Intro
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Generative Deep Learning Book - Preface
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