Learning with Limited Labeled Data with Shioulin Sam - TWiML Talk #255
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Exploring Active Learning and Uncertainty in Deep Neural Networks
This chapter explores active learning in deep neural networks, focusing on how to measure prediction confidence and the limitations of probability scores as indicators of uncertainty. It also discusses various strategies for assessing uncertainty, including adversarial techniques and Bayesian methods, emphasizing the complexities in deep learning models.
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