Search Results for "machine learning python" - Page 15
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Showing 2922 open source projects for "machine learning python"
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Fairlearn
A Python package to assess and improve fairness of ML models
Fairlearn is a Python package that empowers developers of artificial intelligence (AI) systems to assess their system's fairness and mitigate any observed unfairness issues. Fairlearn contains mitigation algorithms as well as metrics for model assessment. Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage. An AI system can behave unfairly for a variety of reasons. In Fairlearn, we define whether an AI system is behaving unfairly in terms... -
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Apache Spark
A unified analytics engine for large-scale data processing
...With Spark Streaming (microbatches) and Structured Streaming, it delivers low-latency event processing suitable for real-time analytics. The built-in MLlib library provides scalable machine learning algorithms, while GraphX enables graph computations integrated with data pipelines. Spark supports multiple languages—Scala, Java, Python, R—and connects with many storage systems like HDFS, S3, Cassandra, and streaming platforms like Kafka, making it a versatile choice for big data workloads in analytics, ETL, and data science. -
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caret
caret (Classification And Regression Training) R package
The caret (Classification And Regression Training) R package streamlines the process of building predictive machine learning models. It provides uniform interfaces for model training, tuning, evaluation, preprocessing, and variable importance. With support for over 200 models, caret is foundational for R workflows in modeling and machine learning. -
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IREE
A retargetable MLIR-based machine learning compiler runtime toolkit
IREE (Intermediate Representation Execution Environment, pronounced as "eerie") is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the data center and down to satisfy the constraints and special considerations of mobile and edge deployments. -
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Linfa
A Rust machine learning framework
linfa aims to provide a comprehensive toolkit to build Machine Learning applications with Rust. Kin in spirit to Python's scikit-learn, it focuses on common preprocessing tasks and classical ML algorithms for your everyday ML tasks. -
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InterpretML
Fit interpretable models. Explain blackbox machine learning
In the beginning, machines learned in darkness, and data scientists struggled in the void to explain them. InterpretML is an open-source package that incorporates state-of-the-art machine-learning interpretability techniques under one roof. With this package, you can train interpretable glass box models and explain black box systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. -
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MLDatasets.jl
Utility package for accessing common Machine Learning datasets
This package represents a community effort to provide a common interface for accessing common Machine Learning (ML) datasets. In contrast to other data-related Julia packages, the focus of MLDatasets.jl is specifically on downloading, unpacking, and accessing benchmark datasets. Functionality for the purpose of data processing or visualization is only provided to a degree that is special to some datasets. -
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Thonny
Python IDE for beginners
Thonny is a Python IDE meant for learning programming. -
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Thinc
A refreshing functional take on deep learning
Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models. Previous versions of Thinc have been running quietly in production in thousands of companies, via both spaCy and Prodigy. We wrote the new version to let users compose,... -
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AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning,... -
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MediaPipe Solutions
Cross-platform, customizable ML solutions
MediaPipe is an open-source framework developed by Google for building cross-platform machine learning pipelines that process audio, video, and other streaming data in real time. The system provides developers with tools and reusable components that allow them to combine multiple machine learning models with preprocessing and postprocessing logic into efficient perception pipelines. These pipelines can run on a wide variety of platforms including mobile devices, desktop systems, web browsers, and embedded edge devices. ... -
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VisualDL
Deep Learning Visualization Toolkit
VisualDL, a visualization analysis tool of PaddlePaddle, provides a variety of charts to show the trends of parameters and visualizes model structures, data samples, histograms of tensors, PR curves , ROC curves and high-dimensional data distributions. It enables users to understand the training process and the model structure more clearly and intuitively so as to optimize models efficiently. VisualDL provides various visualization functions, including tracking metrics in real-time,... -
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Sacred
Sacred is a tool to help you configure, andorganize IDSIA experiments
Sacred is a tool to help you configure, organize, log and reproduce experiments. It is designed to do all the tedious overhead work that you need to do around your actual experiment. A very convenient way of the local variables in a function to define the parameters your experiment uses. You can access all parameters of your configuration from every function. They are automatically injected by name. You get a powerful command-line interface for each experiment that you can use to change... -
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mosaicml composer
Supercharge Your Model Training
composer is a deep learning training framework built on PyTorch and designed to make large-scale model training more efficient, scalable, and customizable. At the center of the project is a highly optimized Trainer abstraction that simplifies the management of training loops, parallelization, metrics, logging, and data loading. The framework is intended for modern workloads that may span anything from a single GPU to very large distributed training environments, which makes it suitable for... -
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Machine Learning Notebooks
Machine Learning Notebooks
Machine Learning Notebooks is an open-source collection of machine learning notebooks designed to provide practical, minimal, and reusable implementations of common AI tasks across different domains. The project focuses on delivering concise, well-structured Jupyter notebooks that demonstrate how to build, train, and evaluate models using modern machine learning frameworks such as PyTorch. -
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Physical Symbolic Optimization (Φ-SO)
Physical Symbolic Optimization
Physical Symbolic Optimization (Φ-SO) - A symbolic optimization package built for physics. Symbolic regression module uses deep reinforcement learning to infer analytical physical laws that fit data points, searching in the space of functional forms. -
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MLJ
A Julia machine learning framework
MLJ (Machine Learning in Julia) is a toolbox written in Julia providing a common interface and meta-algorithms for selecting, tuning, evaluating, composing and comparing about 200 machine learning models written in Julia and other languages. The functionality of MLJ is distributed over several repositories illustrated in the dependency chart below. -
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MiniSom
MiniSom is a minimalistic implementation of the Self Organizing Maps
MiniSom is a minimalistic and Numpy-based implementation of the Self Organizing Maps (SOM). SOM is a type of Artificial Neural Network able to convert complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. Minisom is designed to allow researchers to easily build on top of it and to give students the ability to quickly grasp its details. The project initially aimed for a minimalistic implementation of... -
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HDBSCAN
A high performance implementation of HDBSCAN clustering
HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster... -
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SkyPilot
SkyPilot: Run AI and batch jobs on any infra
SkyPilot is a framework for running AI and batch workloads on any infra, offering unified execution, high cost savings, and high GPU availability. Run AI and batch jobs on any infra (Kubernetes or 12+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface. -
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Denoising Diffusion Probabilistic Model
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to generative modeling that may have the potential to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution. If you simply want to pass in a folder name and the desired image dimensions, you can use the Trainer class to easily train a model. -
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DALI
A GPU-accelerated library containing highly optimized building blocks
The NVIDIA Data Loading Library (DALI) is a library for data loading and pre-processing to accelerate deep learning applications. It provides a collection of highly optimized building blocks for loading and processing image, video and audio data. It can be used as a portable drop-in replacement for built-in data loaders and data iterators in popular deep learning frameworks. Deep learning applications require complex, multi-stage data processing pipelines that include loading, decoding,... -
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Bittensor
Internet-scale Neural Networks
Bittensor is a decentralized machine learning protocol that allows AI models to collaborate, learn, and earn tokens within a global network. It introduces a blockchain-based economy for neural networks, where participants are incentivized to contribute valuable knowledge and compute power. Bittensor combines peer-to-peer learning with on-chain rewards, creating a self-governing, scalable AI system that evolves without centralized control. -
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Angel
A Flexible and Powerful Parameter Server for large-scale ML
Angel is a high-performance distributed machine learning and graph computing platform based on the philosophy of Parameter Server. It is tuned for performance with big data from Tencent and has a wide range of applicability and stability, demonstrating an increasing advantage in handling higher-dimension models. Angel is jointly developed by Tencent and Peking University, taking account of both high availability in industry and innovation in academia. -
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OpenVINO Notebooks
Jupyter notebook tutorials for OpenVINO
...Many notebooks include end-to-end examples that show how to prepare input data, load optimized models, run inference, and visualize results. The project is particularly useful for developers who want to learn how to optimize machine learning inference pipelines for production environments.
