Tag

High-level, multi-backend deep-learning API for building, training and deploying neural-network models.

Provides a consistent Python library of classic ML algorithms, preprocessing, model selection, and evaluation tools. Emphasizes a uniform estimator API, Pipelines, and tight NumPy/SciPy integration—suited for teaching and rapid prototyping of tabular and small-to-medium workloads.

NumPy is the fundamental package for scientific computing in Python. It provides a powerful N-dimensional array object, broadcasting operations, linear algebra, FFT, random number capabilities, and integration with C/C++/Fortran, and serves as a core building block for many AI and scientific libraries.

Provides APIs to build, learn, and run Bayesian and dynamic Bayesian networks, perform probabilistic inference, and compute interventional/counterfactual queries. Ships example notebooks, tutorials, and PyPI/conda packages. ([github.com](https://github.com/pgmpy/pgmpy))

Provides an open-source platform for building, training, and deploying machine learning models across CPU, GPU, and TPU. Includes high-level APIs, model hubs, and tools for production deployment and optimization.

ONNX (Open Neural Network Exchange) is an open ecosystem that provides an open source format for AI models, including deep learning and traditional ML. It defines an extensible computation graph model, built-in operators, and standard data types, focusing on inferencing capabilities. Widely supported across frameworks and hardware, it enables interoperability and accelerates AI innovation.

Orchestrates and scales Python-based AI/ML workloads from laptop to thousands of GPUs — exposing task and actor primitives plus high-level libraries for training, hyperparameter tuning, serving, RL, and data processing. Designed for heterogeneous accelerators and production ML pipelines.

Optimizes and deploys deep learning models for inference across CPU, GPU and Intel NPUs. Converts models from PyTorch/TensorFlow/ONNX, exposes C++/Python/NodeJS APIs, and integrates with Hugging Face and Optimum for LLM/GenAI workloads.

Provides a Python framework for building and training hybrid quantum–classical models via differentiable programming, with integrations for PyTorch, TensorFlow, and JAX. Supports hardware plugins, parameter‑shift gradients, quantum‑chemistry tools, and high‑performance simulators for research workflows.

Simplifies training and scaling PyTorch models by providing a lightweight, opinionated wrapper for training loops, distributed strategies, and experiment orchestration. Modular components and integrations let you prototype on one GPU and scale to multi-node setups with minimal code changes.

Provides a PyTorch-based framework for building, training, and deploying speech and multimodal models (ASR, TTS, speech LLMs). Includes NVIDIA-optimized components, pre-trained checkpoints, streaming/low-latency features, and Hugging Face integration to accelerate prototyping to production.

An AI-native, weight-centric infrastructure for quantitative trading that produces target portfolio weight vectors to unify data ingestion, strategy composition, backtesting, and live/broker execution. Modular pipeline supports ML/DRL allocators, LLM-ready preprocessing, multi-source data, and Alpaca integration for paper/live trading.