Search Results for "project-open" - Page 8
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Showing 1199 open source projects for "project-open"
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Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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Weaviate
Weaviate is a cloud-native, modular, real-time vector search engine
Weaviate in a nutshell: Weaviate is a vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale. Weaviate in detail: Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer-Extraction, Classification, Customizable... -
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Kornia
Open Source Differentiable Computer Vision Library
Kornia is a differentiable computer vision library for PyTorch. It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within... -
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stable-diffusion-videos
Create videos with Stable Diffusion
Create videos with Stable Diffusion by exploring the latent space and morphing between text prompts. Try it yourself in Colab. -
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Aim
An easy-to-use & supercharged open-source experiment tracker
...The Aim standard package comes with all integrations. If you'd like to modify the integration and make it custom, create a new integration package and share with others. Aim is an open-source, self-hosted AI Metadata tracking tool designed to handle 100,000s of tracked metadata sequences. The two most famous AI metadata applications are: experiment tracking and prompt engineering. Aim provides a performant and beautiful UI for exploring and comparing training runs, and prompt sessions. -
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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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. We expect these tools will be used as a base around... -
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TensorFlow Model Optimization Toolkit
A toolkit to optimize ML models for deployment for Keras & TensorFlow
The TensorFlow Model Optimization Toolkit is a suite of tools for optimizing ML models for deployment and execution. Among many uses, the toolkit supports techniques used to reduce latency and inference costs for cloud and edge devices (e.g. mobile, IoT). Deploy models to edge devices with restrictions on processing, memory, power consumption, network usage, and model storage space. Enable execution on and optimize for existing hardware or new special purpose accelerators. Choose the model... -
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huggingface_hub
The official Python client for the Huggingface Hub
The huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine-learning apps hosted on the Hub. You can also create and share your own models, datasets, and demos with the community. The huggingface_hub library provides a simple way to do all these things with Python. -
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Turing.jl
Bayesian inference with probabilistic programming
Bayesian inference with probabilistic programming. -
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ML Intern
ML engineer that reads papers, trains models, and ships ML models
ML Intern is a repository by Hugging Face that provides educational content and projects aimed at helping learners gain practical experience in machine learning and AI development. It is designed to simulate the experience of working as a machine learning intern, offering tasks and exercises that mirror real-world workflows. The project includes tutorials, datasets, and example implementations that guide users through different aspects of ML development. It emphasizes hands-on learning, encouraging users to build and experiment rather than passively consume information. The repository also introduces tools and libraries commonly used in the Hugging Face ecosystem. ... -
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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Machine Learning Study
This repository is for helping those interested in machine learning
Machine Learning Study is an educational repository containing tutorials and study materials related to machine learning and data science using Python. The project compiles notebooks, explanatory documents, and practical code examples that illustrate common machine learning workflows. Topics covered include supervised learning algorithms, feature engineering, model training, and performance evaluation techniques. The repository is structured as a learning resource that guides readers through building machine learning models step by step. ... -
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NeuralForecast
Scalable and user friendly neural forecasting algorithms.
NeuralForecast offers a large collection of neural forecasting models focusing on their performance, usability, and robustness. The models range from classic networks like RNNs to the latest transformers: MLP, LSTM, GRU, RNN, TCN, TimesNet, BiTCN, DeepAR, NBEATS, NBEATSx, NHITS, TiDE, DeepNPTS, TSMixer, TSMixerx, MLPMultivariate, DLinear, NLinear, TFT, Informer, AutoFormer, FedFormer, PatchTST, iTransformer, StemGNN, and TimeLLM. There is a shared belief in Neural forecasting methods'... -
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Haiku
JAX-based neural network library
Haiku is a library built on top of JAX designed to provide simple, composable abstractions for machine learning research. Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX’s pure function transformations. Haiku is designed to make the common things we do such as managing model parameters and other model state simpler and similar in spirit to the Sonnet library that has been widely used... -
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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. Reproduce any model, with saved... -
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lightning AI
The most intuitive, flexible, way for researchers to build models
Build in days not months with the most intuitive, flexible framework for building models and Lightning Apps (ie: ML workflow templates) which "glue" together your favorite ML lifecycle tools. Build models and build/publish end-to-end ML workflows that "glue" your favorite tools together. Models are “easy”, the “glue” work is hard. Lightning Apps are community-built templates that stitch together your favorite ML lifecycle tools into cohesive ML workflows that can run on your laptop or any... -
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Scanpy
Single-cell analysis in Python
Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly with anndata. It includes preprocessing, visualization, clustering, trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells. -
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Ludwig
A codeless platform to train and test deep learning models
Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. All you need to provide is a CSV file containing your data, a list of columns to use as inputs, and a list of columns to use as outputs, Ludwig will do the rest. Simple commands can be used to train models both locally and in a distributed way, and to use them to predict on new data. -
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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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TNT
A lightweight library for PyTorch training tools and utilities
TNT is a lightweight training framework developed by Meta that simplifies the process of building and managing machine learning training loops using PyTorch. The project focuses on providing a flexible yet structured environment for implementing training pipelines without the complexity of large deep learning frameworks. It introduces modular abstractions that allow developers to organize training logic into reusable components such as trainers, evaluators, and callbacks. This design helps separate concerns such as model training, evaluation, logging, and checkpointing, making machine learning experiments easier to manage. ... -
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Python Programming Hub
Learn Python and Machine Learning from scratch
Python Programming Hub repository by Tanu-N-Prabhu is an educational resource designed to help programmers learn Python programming and data science concepts through practical examples and notebooks. The project contains a wide range of tutorials and exercises that cover Python fundamentals, programming concepts, and applied techniques for data analysis and machine learning. Many sections are implemented as Jupyter notebooks, allowing learners to run code interactively while reading explanations of the concepts involved. The repository emphasizes hands-on learning by demonstrating real programming tasks such as data manipulation, statistical analysis, visualization, and automation. ... -
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course.fast.ai
The fast.ai course notebooks
...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. The repository includes lesson notebooks, slide presentations, spreadsheets, and supplementary materials that help students understand neural networks, computer vision, and natural language processing tasks. ... -
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Data Science Articles from CodeCut
Collection of useful data science topics along with articles
...Instead of providing a single software package, the repository aggregates articles, tutorials, and examples covering many topics within the data science ecosystem. The materials address areas such as MLOps, data management, project organization, testing practices, visualization techniques, and productivity tools used by data scientists. Each topic often includes references to code repositories, demonstrations, and video tutorials that show how the tools can be applied in real projects. The repository is intended to help practitioners stay updated with current best practices and technologies in the field of data science. -
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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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TensorFlow Datasets
TFDS is a collection of datasets ready to use with TensorFlow,
TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data. Datasets , enabling easy-to-use and high-performance input pipelines. To get started see the guide and our list of datasets. -
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mlforecast
Scalable machine learning for time series forecasting
mlforecast is a time-series forecasting framework built around machine-learning models, designed to make forecasting both efficient and scalable. It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates... -
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Avalanche
End-to-End Library for Continual Learning based on PyTorch
Avalanche is an end-to-end Continual Learning library based on Pytorch, born within ContinualAI with the unique goal of providing a shared and collaborative open-source (MIT licensed) codebase for fast prototyping, training and reproducible evaluation of continual learning algorithms. Avalanche can help Continual Learning researchers in several ways. This module maintains a uniform API for data handling: mostly generating a stream of data from one or more datasets. It contains all the major CL benchmarks (similar to what has been done for torchvision). ...