Open Source Python Artificial Intelligence Software - Page 86
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Python Artificial Intelligence Software
View 17708 business solutionsBrowse free open source Python Artificial Intelligence Software and projects below. Use the toggles on the left to filter open source Python Artificial Intelligence Software by OS, license, language, programming language, and project status.
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Nemotron 3 Nano
LL model providing reasoning and conversational capabilities
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 is a mid-sized open large language model created by NVIDIA to provide strong reasoning and conversational capabilities while maintaining efficient deployment requirements. The model contains roughly 30 billion parameters and is designed to balance performance and computational efficiency, making it suitable for developers building AI applications that cannot run extremely large models. It is trained from scratch and built using a hybrid architecture that integrates Transformer attention layers with Mamba-style sequence modeling components inside a Mixture-of-Experts framework. This architecture allows the system to maintain strong reasoning capabilities while improving throughput and reducing the computational cost associated with large context processing. The model is designed as a general-purpose language system capable of handling tasks such as chat interaction, coding assistance, document analysis, and instruction following. -
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Nemotron 3 Super
Open language model developed by NVIDIA as part of Nemotron-3 family
NVIDIA-Nemotron-3-Super-120B-A12B-FP8 is a large-scale open language model developed by NVIDIA as part of the Nemotron-3 family of generative AI systems designed for advanced reasoning, conversational interaction, and agent-based workflows. The model contains approximately 120 billion parameters, but employs a Mixture-of-Experts architecture that activates only a smaller subset of parameters during inference, improving computational efficiency while maintaining high capability. Its architecture combines Transformer attention layers with Mamba state-space components to balance long-context reasoning, memory efficiency, and high-quality language generation. The model is optimized for building AI agents that must perform complex tasks such as planning, tool usage, coding assistance, and multi-step reasoning. -
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Collection of Python scripts providing interactions between the sociological investigation platform and webservices (such as NLP, search engine, web database).
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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NeuTTS Air
NeuTTS model built from small LLM backbones
NeuTTS Air is an open-source collection of on-device text-to-speech speech language models from Neuphonic. It is built for natural-sounding voice generation that can run locally instead of relying on a remote web API. The project emphasizes instant voice cloning, real-time performance, and deployment on smaller devices such as phones, laptops, and Raspberry Pi-class hardware. Its LLM-based architecture is intended to bring more expressive and flexible speech generation to local applications. NeuTTS is especially useful for embedded voice agents, private assistants, toys, accessibility tools, and compliance-sensitive apps. Its main value is making modern voice AI more portable, private, and practical for local deployment. -
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Neural Diversity Machines
A Hybrid Neural Network that implements a diverse transfer functions
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Neural Doodle
Turn your two-bit doodles into fine artworks with deep neural networks
Neural Doodle is an archived creative AI project that implements semantic style transfer with deep neural networks. It can transform simple annotated sketches into detailed images by borrowing visual characteristics from reference artwork or photographs. Semantic maps identify corresponding regions so style patches can be transferred to appropriate parts of a generated image. The software also performs conventional style transfer when semantic annotations are omitted. Additional modes support image analogy and standalone texture synthesis from source images. Users can adjust style strength, semantic weighting, smoothing, output resolution, and other parameters while running the tool on a CPU or CUDA-enabled GPU. -
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Neural Enhance
Super Resolution for images using deep learning
Neural Enhance is an archived deep-learning project for increasing image resolution through neural super-resolution. It can generate enlarged images at 2x or potentially higher scales by synthesizing details learned from training images. The generated details are plausible rather than exact reconstructions of information missing from the original picture. Pretrained models support photo enhancement and JPEG artifact repair. Users can also train custom networks with perceptual and adversarial approaches for specialized datasets. The command-line tool supports batch processing, CPU or NVIDIA GPU execution, and Docker-based deployment for easier setup. -
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Neural Network Intelligence
AutoML toolkit for automate machine learning lifecycle
Neural Network Intelligence is an open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning. NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate feature engineering, neural architecture search, hyperparameter tuning and model compression. The tool manages automated machine learning (AutoML) experiments, dispatches and runs experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different training environments like Local Machine, Remote Servers, OpenPAI, Kubeflow, FrameworkController on K8S (AKS etc.) DLWorkspace (aka. DLTS) AML (Azure Machine Learning) and other cloud options. NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiements. -
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Neural Network signal recognition rtlsdr
Deep learning signal classification (recognition) using rtl-sdr dongle
WARNING: Outdated version here. Everything has been moved to github: https://github.com/randaller/cnn-rtlsdr -
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Neural Networks Collection
Neural Networks Collection
This project implements in C++ a bunch of known Neural Networks. So far the project implements: LVQ in several variants, SOM in several variants, Hopfield network and Perceptron. Other neural network types are planned, but not implemented yet. The project can run in two modes: command line tool and Python 7.2 extension. Currently, Python version appears more functional, as it allows easy interaction with algorithms developed by other people. -
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Neural Photo Editor
A simple interface for editing natural photos
Neural Photo Editor is an experimental machine learning application that demonstrates how generative neural networks can be used as an interactive photo editing tool. The project implements the system described in the research paper Neural Photo Editing with Introspective Adversarial Networks, which introduces a generative model capable of modifying images in semantically meaningful ways. Instead of editing images by directly manipulating pixels, the software allows users to influence changes in the latent space of a trained generative model. This approach enables large and coherent modifications to images while preserving visual realism. The system relies on an Introspective Adversarial Network, a hybrid architecture combining elements of variational autoencoders and generative adversarial networks to improve reconstruction accuracy and generative quality. -
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NeuralProphet
A simple forecasting package
NeuralProphet bridges the gap between traditional time-series models and deep learning methods. It's based on PyTorch and can be installed using pip. A Neural Network based Time-Series model, inspired by Facebook Prophet and AR-Net, built on PyTorch. You can find the datasets used in the tutorials, including data preprocessing examples, in our neuralprophet-data repository. The documentation page may not we entirely up to date. Docstrings should be reliable, please refer to those when in doubt. We are working on an improved documentation. We appreciate any help to improve and update the docs. Lagged regressors (measured features, e.g temperature sensor). Future regressors (in advance known features, e.g. temperature forecast). Country holidays & recurring special events. Sparsity of coefficients through regularization. Plotting for forecast components, model coefficients as well as final predictions. Automatic selection of training-related hyperparameters. -
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NeuralTalk2
Efficient Image Captioning code in Torch, runs on GPU
NeuralTalk2 is a Torch-based image-captioning system that generates natural-language descriptions for images with neural networks. It improves on the original NeuralTalk implementation through batching, GPU acceleration, and a more efficient training pipeline. The model combines convolutional neural network image features with a recurrent neural network language model. It supports fine-tuning the underlying CNN instead of relying only on fixed visual features. Training and evaluation tools are included for datasets such as MS COCO. The repository also contains visualization utilities, checkpoint conversion tools, and experimental video-captioning code. It is now primarily preserved as an educational implementation of an influential neural image-captioning architecture. -
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Neuro-comma
Punctuation restoration production-ready model for Russian language
This library was developed with the idea to help us to create punctuation restoration models to memorize trained parameters, data, training visualization, etc. The Library doesn't use any high-level frameworks, such as PyTorch-lightning or Keras, to reduce the level entry threshold. Feel free to fork this repo and edit model or dataset classes for your purposes. Our team always uses the latest version and features of Python. We started with Python 3.9, but realized, that there is no FastAPI image for Python 3.9. There is several PRs in image repositories, but no response from maintainers. So we decided to change code which we use in production to work with the 3.8 version of Python. In some functions we have 3.9 code, but we still use them, these functions are needed only for development purposes. -
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NeuroKiwi is the application of the Associative Model (AM, previously dubbed AMDroid), being a model storing relational change-detections. The application tries to emulate ground-up knowledge accumulation with limited or no prior knowledge of shapes or patterns.
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Simulator of virtual animals made up of biological neural networks for research in the Computational Neuroscience field.
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NeuroNER
Named-entity recognition using neural networks
Named-entity recognition (NER) aims at identifying entities of interest in the text, such as location, organization and temporal expression. Identified entities can be used in various downstream applications such as patient note de-identification and information extraction systems. They can also be used as features for machine learning systems for other natural language processing tasks. Leverages the state-of-the-art prediction capabilities of neural networks (a.k.a. "deep learning") Is cross-platform, open source, freely available, and straightforward to use. Enables the users to create or modify annotations for a new or existing corpus. Train the neural network that performs the NER. During the training, NeuroNER allows monitoring of the network. Evaluate the quality of the predictions made by NeuroNER. The performance metrics can be calculated and plotted by comparing the predicted labels with the gold labels. -
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NewsNotFound
This is the entire source code for NewsNotFound's article gen process
Our mission is to lead the way in AI journalism by providing completely neutral and unbiased news articles that can be governed by the public. NewsNotFound is a news website located at https://newsnotfound.com. We want to build the most unbiased news platform on the internet. -
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Nextpy
Self-Modifying Framework from the Future
NextPy is a Python-based framework for building AI-powered automation agents, allowing developers to create intelligent, rule-based workflows. -
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NiftyNet
An open-source convolutional neural networks platform for research
An open-source convolutional neural networks platform for medical image analysis and image-guided therapy. NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can get started with established pre-trained networks using built-in tools. Adapt existing networks to your imaging data. Quickly build new solutions to your own image analysis problems. NiftyNet currently supports medical image segmentation and generative adversarial networks. NiftyNet is not intended for clinical use. -
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Nixtla TimeGPT
TimeGPT-1: production ready pre-trained Time Series Foundation Model
TimeGPT is a production ready, generative pretrained transformer for time series. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code. Whether you're a bank forecasting market trends or a startup predicting product demand, TimeGPT democratizes access to cutting-edge predictive insights, eliminating the need for a dedicated team of machine learning engineers. A generative model for time series. TimeGPT is capable of accurately predicting various domains such as retail, electricity, finance, and IoT. -
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Norfair
Lightweight Python library for adding real-time multi-object tracking
Norfair is a customizable lightweight Python library for real-time multi-object tracking. Using Norfair, you can add tracking capabilities to any detector with just a few lines of code. Any detector expressing its detections as a series of (x, y) coordinates can be used with Norfair. This includes detectors performing tasks such as object or keypoint detection. It can easily be inserted into complex video processing pipelines to add tracking to existing projects. At the same time, it is possible to build a video inference loop from scratch using just Norfair and a detector. Supports moving camera, re-identification with appearance embeddings, and n-dimensional object tracking. Norfair provides several predefined distance functions to compare tracked objects and detections. The distance functions can also be defined by the user, enabling the implementation of different tracking strategies. -
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Nougat
Implementation of Nougat Neural Optical Understanding
Nougat is a multi-modal generative modeling framework that bridges vision and text modalities with structured generation control (e.g. layout, scene composition) rather than treating images as flat contexts. It combines object-centric modules with transformer-based reasoning to propose, refine, and render scenes in a generative pipeline. The architecture allows you to specify or prompt a layout (which objects should be where) and then the model fills in appearance, context, lighting, and relations coherently. The design supports interactive editing: you could adjust object positions or types and have the model adapt generation accordingly. Because it integrates structured layout reasoning, Nougat tends to produce more compositional and controllable results than purely unconstrained generative models. -
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NÜWA - Pytorch
Implementation of NÜWA, attention network for text to video synthesis
Implementation of NÜWA, state of the art attention network for text-to-video synthesis, in Pytorch. It also contains an extension into video and audio generation, using a dual decoder approach. It seems as though a diffusion-based method has taken the new throne for SOTA. However, I will continue on with NUWA, extending it to use multi-headed codes + hierarchical causal transformer. I think that direction is untapped for improving on this line of work. In the paper, they also present a way to condition the video generation based on segmentation mask(s). You can easily do this as well, given you train a VQGanVAE on the sketches beforehand. Then, you will use NUWASketch instead of NUWA, which can accept the sketch VAE as a reference. This repository will also offer a variant of NUWA that can produce both video and audio. For now, the audio will need to be encoded manually.
