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TensorFlow Probability is a library for probabilistic reasoning and statistical analysis. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. Since TFP inherits the benefits of TensorFlow, you can build, fit, and deploy a model using a single language throughout the lifecycle of model exploration and production. TFP is open source and available on GitHub. Tools to build deep probabilistic models, including probabilistic layers and a `JointDistribution` abstraction. Variational inference and Markov chain Monte Carlo. A wide selection of probability distributions and bijectors. Optimizers such as Nelder-Mead, BFGS, and SGLD.
Features
- A wide selection of probability distributions and bijectors
- Tools to build deep probabilistic models, including probabilistic layers and a `JointDistribution` abstraction
- Variational inference and Markov chain Monte Carlo
- Optimizers such as Nelder-Mead, BFGS, and SGLD
- You can build, fit, and deploy a model using a single language
- TFP is open source and available on GitHub
Project Samples
