This package includes a variety of tools used to analyze persistence diagrams. It currently houses implementations of

• Persistence images

• Persistence landscapes

• Bottleneck distance

• Modified Gromov–Hausdorff distance

• Sliced Wasserstein kernel

• Heat kernel

• Diagram plotting

Captum is a model interpretability and understanding library for PyTorch. Captum contains general purpose implementations of integrated gradients, saliency maps, smoothgrad, vargrad and others for PyTorch models. It has quick integration for models built with domain-specific libraries such as torchvision, torchtext, and others.

BoTorch is a library for Bayesian Optimization built on PyTorch.

DeepXDE is a library for scientific machine learning and physics-informed learning. It includes implementations for the PINN (physics-informed neural networks), DeepONet (deep operator network) and MFNN (multifidelity neural network) algorithms.

Scikit-rebate is a scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning. These algorithms excel at identifying features that are predictive of the outcome in supervised learning problems, and are especially good at identifying feature interactions that are normally overlooked by standard feature selection algorithms.

This package provides a machine learning library of popular datasets, model architectures, and common transformations to apply python-pytorch in the audio domain.

PyG is a library built upon PyTorch to easily write and train Graph Neural Networks for a wide range of applications related to structured data.

Uniform Manifold Approximation and Projection is a dimension reduction technique that can be used for visualization similarly to t-SNE, but also for general non-linear dimension reduction.

This is a modular Python implementation of t-Distributed Stochastic Neighbor Embedding (t-SNE), a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets.

This is a real-time full-duplex speech recognition server, based on the Kaldi toolkit and the GStreamer framework and implemented in Python.

A Python library for reading and writing GGUF & GGML format ML models.

GPy is a Gaussian Process (GP) framework written in Python, from the Sheffield machine learning group. GPy implements a range of machine learning algorithms based on GPs.

This package provides a generic API for dispatch to Pyro backends.

LinearOperator is a PyTorch package for abstracting away the linear algebra routines needed for structured matrices (or operators).

cleanlab automatically finds and fixes errors in any ML dataset. This data-centric AI package facilitates machine learning with messy, real-world data by providing clean labels during training.

This package provides a standard API for reinforcement learning and a diverse set of reference environments (formerly Gym).

ONNX Runtime is a performance-focused complete scoring engine for Open Neural Network Exchange (ONNX) models, with an open extensible architecture to continually address the latest developments in AI and Deep Learning. ONNX Runtime stays up to date with the ONNX standard with complete implementation of all ONNX operators, and supports all ONNX releases (1.2+) with both future and backwards compatibility.

This package provides simple access speech to text for using in Linux without being tied to a desktop environment, using the vosk-api. The user configuration lets you manipulate text using Python string operations. It has zero overhead, as this relies on manual activation and there are no background processes. Dictation is accessed manually with nerd-dictation begin and nerd-dictation end commands.

This package is a toolbox for optimization on Riemannian manifolds with support for automatic differentiation.

The MCL algorithm is short for the Markov Cluster Algorithm, a fast and scalable unsupervised cluster algorithm for graphs (also known as networks) based on simulation of (stochastic) flow in graphs.

This package provides a tool for visualizing live, rich data for Torch and Numpy.

This package provides a functional take on deep learning, compatible with your favorite libraries.

fastText is a library for efficient learning of word representations and sentence classification.

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.