zuko and normalizing-flows
These are competitors offering mutually exclusive framework choices—Zuko implements normalizing flows in PyTorch while the other implements them in TensorFlow 2, so practitioners must select one based on their preferred deep learning framework rather than using both together.
About zuko
probabilists/zuko
Normalizing flows in PyTorch
This project helps machine learning engineers and researchers build advanced probabilistic models. It takes in structured data and outputs flexible, high-dimensional probability distributions that can be easily trained and sampled. It is ideal for those working on complex density estimation or generative modeling tasks.
About normalizing-flows
LukasRinder/normalizing-flows
Implementation of normalizing flows in TensorFlow 2 including a small tutorial.
This project offers tools to build advanced AI models that can accurately estimate the probability distribution of complex data or generate new, realistic data samples from scratch. You can input various datasets, like sensor readings, financial time series, or images, and output either a detailed understanding of the data's underlying patterns or entirely new data that mimics the original. This is ideal for machine learning researchers, data scientists, and AI developers working on generative models or anomaly detection.
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