Search Results for "learning" - Page 4
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Showing 1279 open source projects for "learning"
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tvm
Open deep learning compiler stack for cpu, gpu, etc.
Apache TVM is an open source machine learning compiler framework for CPUs, GPUs, and machine learning accelerators. It aims to enable machine learning engineers to optimize and run computations efficiently on any hardware backend. The vision of the Apache TVM Project is to host a diverse community of experts and practitioners in machine learning, compilers, and systems architecture to build an accessible, extensible, and automated open-source framework that optimizes current and emerging machine learning models for any hardware platform. ... -
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AI_Tutorial
A selection of learning materials, search, recommendation, advertising
AI_Tutorial is a large curated repository that aggregates high-quality learning resources related to artificial intelligence, machine learning, deep learning, natural language processing, and data engineering. The project functions as a centralized knowledge base designed to help engineers and researchers discover tutorials, technical articles, algorithm explanations, and architecture discussions from across the AI ecosystem. -
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TorchRL
A modular, primitive-first, python-first PyTorch library
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. TorchRL provides PyTorch and python-first, low and high-level abstractions for RL that are intended to be efficient, modular, documented, and properly tested. The code is aimed at supporting research in RL. Most of it is written in Python in a highly modular way, such that researchers can easily swap components, transform them, or write new ones with little effort. -
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Stable Baselines3
PyTorch version of Stable Baselines
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines. You can read a detailed presentation of Stable Baselines3 in the v1.0 blog post or our JMLR paper. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. -
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RL with PyTorch
Clean, Robust, and Unified PyTorch implementation
RL with PyTorch is a research-oriented repository that provides implementations of deep reinforcement learning algorithms using the PyTorch framework. The project focuses on helping developers and researchers understand reinforcement learning methods by providing clean and reproducible implementations of well-known algorithms. It includes code for popular deep reinforcement learning techniques such as Deep Q-Networks, policy gradient methods, actor-critic architectures, and other modern RL approaches. ... -
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Metaflow
A framework for real-life data science
...Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. -
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MNN
MNN is a blazing fast, lightweight deep learning framework
MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models, and has industry leading performance for inference and training on-device. At present, MNN has been integrated in more than 20 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control. ... -
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CatBoost
High-performance library for gradient boosting on decision trees
CatBoost is a fast, high-performance open source library for gradient boosting on decision trees. It is a machine learning method with plenty of applications, including ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. CatBoost offers superior performance over other GBDT libraries on many datasets, and has several superb features. It has best in class prediction speed, supports both numerical and categorical features, has a fast and scalable GPU version, and readily comes with visualization tools. ... -
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PyCaret
An open-source, low-code machine learning library in Python
PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive. In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. -
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TPOT
A Python Automated Machine Learning tool that optimizes ML
Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. -
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DGL
Python package built to ease deep learning on graph
...Scale to giant graphs via multi-GPU acceleration and distributed training infrastructure. DGL empowers a variety of domain-specific projects including DGL-KE for learning large-scale knowledge graph embeddings, DGL-LifeSci for bioinformatics and cheminformatics, and many others. We are keen to bringing graphs closer to deep learning researchers. We want to make it easy to implement graph neural networks model family. We also want to make the combination of graph based modules and tensor based modules (PyTorch or MXNet) as smooth as possible. ... -
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ml.js
Machine learning tools in JavaScript
This library is a compilation of the tools developed in the mljs organization. It is mainly maintained for use in the browser. If you are working with Node.js, you might prefer to add to your dependencies only the libraries that you need, as they are usually published to npm more often. We prefix all our npm package names with ml- (eg. ml-matrix) so they are easy to find. -
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dlib
Toolkit for making machine learning and data analysis applications
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. 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. Dlib's open source licensing allows you to use it in any application, free of charge. -
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Orion
A machine learning library for detecting anomalies in signals
Orion is a machine-learning library built for unsupervised time series anomaly detection. Such signals are generated by a wide variety of systems, few examples include telemetry data generated by satellites, signals from wind turbines, and even stock market price tickers. We built this to provide one place where users can find the latest and greatest in machine learning and deep learning world including our own innovations. -
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Rubix ML
A high-level machine learning and deep learning library for PHP
Rubix ML is a free open-source machine learning (ML) library that allows you to build programs that learn from your data using the PHP language. We provide tools for the entire machine learning life cycle from ETL to training, cross-validation, and production with over 40 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects. -
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Weights and Biases
Tool for visualizing and tracking your machine learning experiments
Use W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production models. Quickly identify model regressions. Use W&B to visualize results in real time, all in a central dashboard. Focus on the interesting ML. Spend less time manually tracking results in spreadsheets and text files. Capture dataset versions with W&B Artifacts to identify how changing data affects your resulting models. -
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MatlabMachine
Machine learning algorithms
Matlab-Machine is a comprehensive collection of machine learning algorithms implemented in MATLAB. It includes both basic and advanced techniques for classification, regression, clustering, and dimensionality reduction. Designed for educational and research purposes, the repository provides clear implementations that help users understand core ML concepts. -
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TNT
A lightweight library for PyTorch training tools and utilities
...This design helps separate concerns such as model training, evaluation, logging, and checkpointing, making machine learning experiments easier to manage. The framework is particularly useful for large-scale experiments where maintaining clear training workflows becomes increasingly important. Because it is built on top of PyTorch, the framework integrates naturally with existing deep learning models and datasets. -
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Model Zoo
Please do not feed the models
FluxML Model Zoo is a collection of demonstration models built with the Flux machine learning library in Julia. The repository provides ready-to-run implementations across multiple domains, including computer vision, natural language processing, and reinforcement learning. Each model is organized into its own project folder with pinned package versions, ensuring reproducibility and stability. The examples serve both as educational tools for learning Flux and as practical starting points for building new models. ... -
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Gradio
Create UIs for your machine learning model in Python in 3 minutes
Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. -
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Flower
Flower: A Friendly Federated Learning Framework
A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language. Federated learning systems vary wildly from one use case to another. Flower allows for a wide range of different configurations depending on the needs of each individual use case. Flower originated from a research project at the University of Oxford, so it was built with AI research in mind. -
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hls4ml
Machine learning on FPGAs using HLS
hls4ml is an open-source framework that enables machine learning models to be implemented directly on hardware such as FPGAs and ASICs using high-level synthesis techniques. The system converts trained neural network models from common machine learning frameworks into hardware description code suitable for ultra-low-latency inference. This approach allows machine learning algorithms to run directly on specialized hardware, making them suitable for applications that require extremely fast response times and minimal power consumption. ... -
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Book6_First-Course-in-Data-Science
From Addition, Subtraction, Multiplication, and Division to ML
...The material emphasizes a learning approach that combines theoretical knowledge with hands-on experimentation, often recommending interactive tools such as Jupyter notebooks to explore the ideas presented in the book. -
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course.fast.ai
The fast.ai course notebooks
course22 is the official repository containing the notebooks, slides, and supporting materials for the 2022 edition of the fast.ai course Practical Deep Learning for Coders. The repository serves as the core educational resource for the course, providing learners with hands-on exercises and coding tutorials that accompany each lecture. The project emphasizes learning deep learning through experimentation rather than purely theoretical study, encouraging students to build models and analyze results directly in Jupyter notebooks. ... -
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MLOps Zoomcamp
Free MLOps course from DataTalks.Club
MLOps Zoomcamp is an open-source educational repository that contains the materials for a free course focused on machine learning operations and production machine learning systems. The course is designed to teach data scientists and engineers how to move machine learning models from experimentation environments into scalable production services. The repository provides lessons, code examples, and assignments that cover the entire MLOps lifecycle, including model training, experiment tracking, deployment, monitoring, and infrastructure management. ...
