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

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.

This package provides a collection of ordinal regression models for machine learning in Python. They are intended to be used with scikit-learn and are compatible with its API.

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.

Visualization and NeuroML import/export tools for the Brian 2 simulator.

QNNPACK is a library for low-precision neural network inference. It contains the implementation of common neural network operators on quantized 8-bit tensors.

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.

XNNPACK is a highly optimized library of floating-point neural network inference operators for ARM, WebAssembly, and x86 platforms. XNNPACK is not intended for direct use by deep learning practitioners and researchers; instead it provides low-level performance primitives for accelerating high-level machine learning frameworks, such as TensorFlow Lite, TensorFlow.js, PyTorch, and MediaPipe.

TensorFlow is a flexible platform for building and training machine learning models. This package provides the "lite" variant for mobile devices.

This package provides a C++ and Python library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes.

Not all possible optimizations can be directly implemented on ONNX graphs--- some will need additional backend-specific information---but many can, and the aim is to provide all such passes along with ONNX so that they can be re-used with a single function call.

Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions.

TorchMetrics is a collection of 100+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. It offers:

• A standardized interface to increase reproducibility

• Reduces boilerplate

• Automatic accumulation over batches

• Metrics optimized for distributed-training

• Automatic synchronization between multiple devices

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.

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 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

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.

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

This package provides a tensor-like library for functions and distributions.

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

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.

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 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.

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.