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Showing 19 open source projects for "rich"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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pmdarima
Statistical library designed to fill the void in Python's time series
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function. -
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Albumentations
Fast image augmentation library and an easy-to-use wrapper
Albumentations is a computer vision tool that boosts the performance of deep convolutional neural networks. Albumentations is a Python library for fast and flexible image augmentations. Albumentations efficiently implements a rich variety of image transform operations that are optimized for performance, and does so while providing a concise, yet powerful image augmentation interface for different computer vision tasks, including object classification, segmentation, and detection. Albumentations supports different computer vision tasks such as classification, semantic segmentation, instance segmentation, object detection, and pose estimation. ... -
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Learning Interpretability Tool
Interactively analyze ML models to understand their behavior
The Learning Interpretability Tool (LIT, formerly known as the Language Interpretability Tool) is a visual, interactive ML model-understanding tool that supports text, image, and tabular data. It can be run as a standalone server, or inside of notebook environments such as Colab, Jupyter, and Google Cloud Vertex AI notebooks. -
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Unity ML-Agents Toolkit
Unity machine learning agents toolkit
...Advancement of artificial intelligence (AI) research depends on figuring out tough problems in existing environments using current benchmarks for training AI models. Using Unity and the ML-Agents toolkit, you can create AI environments that are physically, visually, and cognitively rich. -
99.99% Uptime for MySQL and PostgreSQL DatabasesCloud SQL Enterprise Plus delivers near-zero downtime with 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server.
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The Julia Programming Language
High-level, high-performance dynamic language for technical computing
Julia is a fast, open source high-performance dynamic language for technical computing. It can be used for data visualization and plotting, deep learning, machine learning, scientific computing, parallel computing and so much more. Having a high level syntax, Julia is easy to use for programmers of every level and background. Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose... -
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PaddlePaddle
PArallel Distributed Deep LEarning: Machine Learning Framework
PaddlePaddle is an open source deep learning industrial platform with advanced technologies and a rich set of features that make innovation and application of deep learning easier. It is the only independent R&D deep learning platform in China, and has been widely adopted in various sectors including manufacturing, agriculture and enterprise service. PaddlePaddle covers core deep learning frameworks, basic model libraries, end-to-end development kits and more, with support for both dynamic and static graphs. -
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mlr3
mlr3: Machine Learning in R - next generation
mlr3 is a modern, object-oriented R framework for machine learning. It provides core abstractions (tasks, learners, resamplings, measures, pipelines) implemented using R6 classes, enabling extensible, composable machine learning workflows. It focuses on clean design, scalability (large datasets), and integration into the wider R ecosystem via extension packages. Users can do classification, regression, survival analysis, clustering, hyperparameter tuning, benchmarking etc., often via... -
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dm_control
DeepMind's software stack for physics-based simulation
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo. DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo physics. The MuJoCo Python bindings support three different OpenGL rendering backends: EGL (headless, hardware-accelerated), GLFW (windowed, hardware-accelerated), and OSMesa (purely software-based). At least one of these three backends must be available in order render... -
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Darts
A python library for easy manipulation and forecasting of time series
...Darts supports both univariate and multivariate time series and models. The ML-based models can be trained on potentially large datasets containing multiple time series, and some of the models offer a rich support for probabilistic forecasting. We recommend to first setup a clean Python environment for your project with at least Python 3.7 using your favorite tool (conda, venv, virtualenv with or without virtualenvwrapper). -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Computer Vision in Action
A computer vision closed-loop learning platform
Computer Vision in Action is a practical, example-rich repository that demonstrates real-world applications of computer vision techniques and algorithms in Python, often using OpenCV, deep learning models, and related tooling. It serves as a hands-on companion for learners and engineers who want to understand not just the theory, but how computer vision is actually implemented for tasks like object detection, image classification, feature tracking, optical flow, and image segmentation. ... -
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UnionML
Build and deploy machine learning microservices
...Combine the tools that you love using a simple, standardized API so you can stop writing so much boilerplate and focus on what matters: the data and the models that learn from them. Fit the rich ecosystem of tools and frameworks into a common protocol for machine learning. Using industry-standard machine learning methods, implement endpoints for fetching data, training models, serving predictions (and much more) to write a complete ML stack in one place. Data science, ML engineering, and MLOps practitioners can all gather around UnionML apps as a way of defining a single source of truth about your ML system’s behavior. ... -
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PySC2
StarCraft II learning environment
...It exposes Blizzard Entertainment's StarCraft II Machine Learning API as a Python RL Environment. This is a collaboration between DeepMind and Blizzard to develop StarCraft II into a rich environment for RL research. PySC2 provides an interface for RL agents to interact with StarCraft 2, getting observations and sending actions. The easiest way to get PySC2 is to use pip. That will install the pysc2 package along with all the required dependencies. virtualenv can help manage your dependencies. You may also need to upgrade pip: pip install --upgrade pip for the pysc2 install to work. ... -
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CrypTen
A framework for Privacy Preserving Machine Learning
CrypTen is a research framework developed by Facebook Research for privacy-preserving machine learning built directly on top of PyTorch. It provides a secure and intuitive environment for performing computations on encrypted data using Secure Multiparty Computation (SMPC). Designed to make secure computation accessible to machine learning practitioners, CrypTen introduces a CrypTensor object that behaves like a regular PyTorch tensor, allowing users to seamlessly apply automatic... -
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Python Machine Learning
The "Python Machine Learning (2nd edition)" book code repository
...Each chapter has Jupyter notebooks and Python scripts that replicate the examples in the book, allowing readers to run, inspect, and tweak code directly as they follow material. The structure also includes errata documentation and assets (images) that appear in the printed edition, providing a rich supplement to learning. The repository is suitable both for classroom use and for self-study, as well as being a go-to reference for data scientists revisiting techniques. -
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Texar
Toolkit for Machine Learning, Natural Language Processing
...Two Versions, (Mostly) Same Interfaces. Texar-TensorFlow (this repo) and Texar-PyTorch have mostly the same interfaces. Both further combine the best design of TF and PyTorch. Rich Pre-trained Models, Rich Usage with Uniform Interfaces. BERT, GPT2, XLNet, etc, for encoding, classification, generation, and composing complex models with other Texar components! -
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lgo
Interactive Go programming with Jupyter
lgo is an open-source programming environment that enables interactive Go programming within Jupyter Notebook environments. The project provides a Jupyter kernel for the Go programming language, allowing developers to write and execute Go code interactively in notebook cells similar to how Python is used in data science workflows. This environment combines the strong performance and concurrency features of the Go language with the exploratory and iterative style of notebook-based... -
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Grenade
Deep Learning in Haskell
Grenade is a composable, dependently typed, practical, and fast recurrent neural network library for concise and precise specifications of complex networks in Haskell. Because the types are so rich, there's no specific term level code required to construct this network; although it is of course possible and easy to construct and deconstruct the networks and layers explicitly oneself. Networks in Grenade can be thought of as a heterogeneous list of layers, where their type includes not only the layers of the network but also the shapes of data that are passed between the layers. ... -
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Python Machine Learning book
The book code repository and info resource
What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning. From theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano. ... -
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mlpy
Machine Learning Python
mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and of GSL. mlpy provides high-level functions and classes allowing, with few lines of code, the design of rich workflows for classification, regression, clustering and feature selection. mlpy is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3. mlpy is available both for Python >=2.6 and Python 3.X.
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