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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...

PyTorch-NLP is a library for Natural Language Processing (NLP) in Python. It’s built with the very latest research in mind, and was designed from day one to support rapid prototyping. PyTorch-NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders. It’s open-source software, released under the BSD3 license. With your batch in hand, you can use PyTorch to develop and train your model using gradient descent. For example, check out...

The Janelia Automatic Animal Behavior Annotator (JAABA) is a machine learning-based system that enables researchers to automatically compute interpretable, quantitative statistics describing video of behaving animals. Through our system, users encode their intuition about the structure of behavior by labeling the behavior of the animal, e.g. walking, grooming, or following, in a small set of video frames. JAABA uses machine learning techniques to convert these manual labels into behavior...

Stable Graphical Model Learning (StabLe) is an algorithm for learning the structure and parameters of stable graphical (SG) models from data. Stable random variables are motivated by the central limit theorem for densities with (potentially) unbounded variance and can be thought of as natural generalizations of the Gaussian distribution to skewed and heavy-tailed phenomenon. SG models are multi-variate stable distributions that represent Bayesian networks whose edges encode linear...

Clever Algorithms collects clear, cookbook-style descriptions of nature-inspired optimization and learning methods, organized so you can pick an algorithm and apply it quickly. Each entry follows a consistent template: motivation, strategy, pseudocode, parameter choices, variations, and references, making it easy to compare approaches. The catalog spans evolutionary algorithms, swarm intelligence, immune systems, simulated annealing, tabu search, and other metaheuristics, plus guidance on...