probability and Probabilistic-Deep-Learning-with-TensorFlow
TensorFlow Probability is a core probabilistic inference library that provides the fundamental building blocks (distributions, bijectors, variational inference), while the Probabilistic Deep Learning repository is an educational resource and application example that demonstrates how to use those TensorFlow Probability tools for practical use cases like autonomous vehicles and medical diagnosis.
About probability
tensorflow/probability
Probabilistic reasoning and statistical analysis in TensorFlow
This project helps data scientists, statisticians, and machine learning engineers analyze data using advanced probabilistic methods. You input your raw data and specify a statistical model (like a mixed-effects model or a Bayesian neural network), and it outputs insights, predictions, or classifications with quantified uncertainty. It's designed for those who need to understand not just 'what' but 'how certain' about their data.
About Probabilistic-Deep-Learning-with-TensorFlow
mohd-faizy/Probabilistic-Deep-Learning-with-TensorFlow
Probabilistic Deep Learning finds its application in autonomous vehicles and medical diagnoses. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real-world datasets.
This project helps data scientists and machine learning engineers build more reliable AI models by quantifying uncertainty. It takes standard datasets and outputs predictions that include a confidence level, which is crucial for critical applications. The user would be someone involved in developing AI solutions where risk assessment and robust decision-making are paramount, such as in autonomous systems or healthcare.
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