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

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

SpaCy is a library for advanced Natural Language Processing in Python and Cython. It comes with pretrained pipelines and currently supports tokenization and training for 70+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management.

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

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.

OpenMM is a toolkit for molecular simulation. It can be used either as a stand-alone application for running simulations, or as a library you call from your own code.

This package provides OCaml bindings for the MCL graph clustering algorithm.

The GeomLoss library provides efficient GPU implementations for:

• Kernel norms (also known as Maximum Mean Discrepancies).

• Hausdorff divergences, which are positive definite generalizations of the Chamfer-ICP loss and are analogous to log-likelihoods of Gaussian Mixture Models.

• Debiased Sinkhorn divergences, which are affordable yet positive and definite approximations of Optimal Transport (Wasserstein) distances.

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.

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 is a high-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model, implemented in plain C/C++ without dependencies, with

• AVX intrinsics support for x86 architectures

• VSX intrinsics support for POWER architectures

• Mixed F16 / F32 precision

• 4-bit and 5-bit integer quantization support

• Zero memory allocations at runtime

• Support for CPU-only inference

• Efficient GPU support for NVIDIA

• OpenVINO Support

• C-style API

This package provides a port to Facebook's LLaMA collection of foundation language models. It requires models parameters to be downloaded independently to be able to run a LLaMA model.

GPyTorch is a Gaussian process library implemented using PyTorch.

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.

PyNNDescent provides a Python implementation of Nearest Neighbor Descent for k-neighbor-graph construction and approximate nearest neighbor search.

OneAPI Deep Neural Network Library (oneDNN) is a cross-platform performance library of basic building blocks for deep learning applications.

Vowpal Wabbit is a machine learning system with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

Interpretable ML (iML) is a set of data type objects, visualizations, and interfaces that can be used by any method designed to explain the predictions of machine learning models (or really the output of any function). It currently contains the interface and IO code from the Shap project, and it will potentially also do the same for the Lime project.

Dlib is a modern C++ toolkit containing machine learning algorithms and tools. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments.

This package provides Autograd-compatible approximations to the gamma family of functions.

LIBSVM is a machine learning library for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

PyTorch is a Python package that provides two high-level features:

• tensor computation (like NumPy) with strong GPU acceleration;

• deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

This package provides a Python library for probabilistic modeling and inference.

This package provides logging utilities for the SpaCy natural language processing framework.