Search Results for "math with python learn" - Page 2
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Showing 341 open source projects for "math with python learn"
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Datasets
Hub of ready-to-use datasets for ML models
Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. We also feature a deep... -
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Flask-SQLAlchemy
Adds SQLAlchemy support to Flask
Flask-SQLAlchemy is an extension for Flask that adds support for SQLAlchemy to your application. It simplifies using SQLAlchemy with Flask by setting up common objects and patterns for using those objects, such as a session tied to each web request, models, and engines. Flask-SQLAlchemy does not change how SQLAlchemy works or is used. See the SQLAlchemy documentation to learn how to work with the ORM in depth. The documentation here will only cover setting up the extension, not how to use... -
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HyperTools
A Python toolbox for gaining geometric insights
HyperTools is a library for visualizing and manipulating high-dimensional data in Python. It is built on top of matplotlib (for plotting), seaborn (for plot styling), and scikit-learn (for data manipulation). Functions for plotting high-dimensional datasets in 2/3D. Static and animated plots. Simple API for customizing plot styles. Set of powerful data manipulation tools including hyperalignment, k-means clustering, normalizing and more. Support for lists of Numpy arrays, Pandas dataframes... -
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sktime
A unified framework for machine learning with time series
sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes time series classification, regression, clustering, annotation, and forecasting. It comes with time series algorithms and scikit-learn compatible tools to build, tune and validate time series models. Our objective is to enhance the interoperability and usability of the time series analysis ecosystem in its entirety. sktime provides a unified... -
Build Securely on Azure with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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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. It is a novel approach to aligning incentives in AI... -
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Dask
Parallel computing with task scheduling
Dask is a Python library for parallel and distributed computing, designed to scale analytics workloads from single machines to large clusters. It integrates with familiar tools like NumPy, Pandas, and scikit-learn while enabling execution across cores or nodes with minimal code changes. Dask excels at handling large datasets that don’t fit into memory and is widely used in data science, machine learning, and big data pipelines. -
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Instructor
Structured outputs for llms
... of validators and custom error messages, enhancing data validation processes. Instructor is trusted by engineers from platforms like Langflow, underscoring its reliability and effectiveness in managing structured outputs powered by LLMs. Instructor is powered by Pydantic, which is powered by type hints. Schema validation and prompting are controlled by type annotations; less to learn, and less code to write, and it integrates with your IDE. -
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SaltStack
Automate the management and configuration of any infrastructure
... an event infrastructure that can drive reactive provisioning, configuration, and management across all systems in your infrastructure. Salt contains a robust and flexible configuration management framework that allows effortless, simultaneous configuration of tens of thousands of systems. Learn about the fundamental components and concepts that you need to understand to use Salt. -
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Oppia
A free, online learning platform to make quality education accessible
... their knowledge. Check out our math lessons with proven results! In addition to developing the Oppia platform, the team is also developing and piloting a set of free and effective lessons on basic mathematics. These lessons are targeted at learners who lack access to educational resources. Oppia is written using Python and AngularJS, and is built on top of Google App Engine. -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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HelloGitHub
Share interesting, entry-level open source projects on GitHub
... found in the process of browsing GitHub, so that it would be easier to find and learn later. Later, I plan to share these interesting and valuable open source projects with you. I ended up writing this website for easy viewing and sharing. Open source projects in various languages, tools to make life better, books, study notes, tutorials, and more. Through these projects, you will learn more programming knowledge, improve your programming skills, and discover the joy of programming. -
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supervision
We write your reusable computer vision tools
We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us. -
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Darts
A python library for easy manipulation and forecasting of time series
darts is a Python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The models can all be used in the same way, using fit() and predict() functions, similar to scikit-learn. The library also makes it easy to backtest models, combine the predictions of several models, and take external data into account. Darts supports both univariate and multivariate time series and models. The ML-based models can... -
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D2L.ai
Interactive deep learning book with multi-framework code
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge. This open-source book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. Offers sufficient technical depth... -
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Flama
Fire up your models with the flame
Flama is a python library which establishes a standard framework for development and deployment of APIs with special focus on machine learning (ML). The main aim of the framework is to make ridiculously simple the deployment of ML APIs, simplifying (when possible) the entire process to a single line of code. The library builds on Starlette, and provides an easy-to-learn philosophy to speed up the building of highly performant GraphQL, REST and ML APIs. Besides, it comprises an ideal solution... -
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Mem0
The Memory layer for AI Agents
... by leveraging historical context, and offering easy integration compatible with platforms like OpenAI and Claude. Mem0 is perfect for projects such as customer support, where chatbots remember past interactions to reduce repetition and speed up resolution times; personal AI companions that recall preferences and past conversations for more meaningful interactions; AI agents that learn from each interaction to become more personalized and effective over time. -
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BentoML
Unified Model Serving Framework
BentoML simplifies ML model deployment and serves your models at a production scale. Support multiple ML frameworks natively: Tensorflow, PyTorch, XGBoost, Scikit-Learn and many more! Define custom serving pipeline with pre-processing, post-processing and ensemble models. Standard .bento format for packaging code, models and dependencies for easy versioning and deployment. Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference workloads... -
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Interpretable machine learning
Book about interpretable machine learning
This book is about interpretable machine learning. Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. An explanation increases the trust in the decision and in the machine learning model. As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which... -
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Pfl Research
Simulation framework for accelerating research
A fast, modular Python framework released by Apple for privacy-preserving federated learning (PFL) simulation. Integrates with TensorFlow, PyTorch, and classical ML, and offers high-speed distributed simulation (7–72× faster than alternatives). -
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bbox-visualizer
Make drawing and labeling bounding boxes easy as cake
Make drawing and labeling bounding boxes easy as cake. This package helps users draw bounding boxes around objects, without doing the clumsy math that you'd need to do for positioning the labels. It also has a few different types of visualizations you can use for labeling objects after identifying them. There are optional functions that can draw multiple bounding boxes and/or write multiple labels on the same image, but it is advisable to use the above functions in a loop in order to have full... -
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Adversarial Robustness Toolbox
Adversarial Robustness Toolbox (ART) - Python Library for ML security
Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to evaluate, defend, certify and verify Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks (TensorFlow, Keras, PyTorch, MXNet, sci-kit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types (images, tables, audio, video... -
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Copulas
A library to model multivariate data using copulas
Copulas is a Python library for modeling multivariate distributions and sampling from them using copula functions. Given a table of numerical data, use Copulas to learn the distribution and generate new synthetic data following the same statistical properties. Choose from a variety of univariate distributions and copulas – including Archimedian Copulas, Gaussian Copulas and Vine Copulas. Compare real and synthetic data visually after building your model. Visualizations are available as 1D... -
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SuperAGI
A dev-first open source autonomous AI agent framework
An open-source autonomous AI framework to enable you to develop and deploy useful autonomous agents quickly & reliably. Join a community of developers constantly contributing to make SuperAGI better. Access your agents through a graphical user interface. Interact with agents by giving them input, permissions, etc. Agents typically learn and improve their performance over time with feedback loops. Run multiple agents simultaneously to improve efficiency and productivity. Connect to multiple... -
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PyCaret
An open-source, low-code machine learning library in Python
.... PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, Optuna, Hyperopt, Ray, and few more. The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more technical expertise. -
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dude uncomplicated data extraction
dude uncomplicated data extraction: A simple framework
Dude is a very simple framework for writing web scrapers using Python decorators. The design, inspired by Flask, was to easily build a web scraper in just a few lines of code. Dude has an easy-to-learn syntax. Dude is currently in Pre-Alpha. Please expect breaking changes. You can run your scraper from terminal/shell/command-line by supplying URLs, the output filename of your choice and the paths to your python scripts to dude scrape command. -
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Full Stack FastAPI and PostgreSQL
Full stack, modern web application generator
Generate a backend and frontend stack using Python, including interactive API documentation. Production ready Python web server using Uvicorn and Gunicorn. Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). Great editor support. Completion everywhere. Less time debugging. Designed to be easy to use and learn. Less time reading docs. Minimize code duplication. Multiple features from each parameter declaration. Get production-ready code. With automatic...
